17.1.3.7.3 Models, Inference, Learning Human Activities, Human Behavior

Chapter Contents (Back)
Activity Recognition. Action Recognition. Human Activity. Human Behavior. Human Motion. Learning.
See also Human Activity Recognition, Human Behaviors.

Galata, A.[Aphrodite], Johnson, N.[Neil], Hogg, D.C.[David C.],
Learning Variable-Length Markov Models of Behavior,
CVIU(81), No. 3, March 2001, pp. 398-413.
DOI Link 0001
BibRef
Earlier:
Learning Structured Behavior Models using Variable Length Markov Models,
MPeople99(xx-yy). BibRef
And:
Learning Behaviour Models of Human Activities,
BMVC99(Modelling Human Behaviour).
PDF File. BibRef
Earlier: A2, A1, A3:
The Acquisition and Use of Interaction Behaviour Models,
CVPR98(866-871).
IEEE DOI BibRef

Hu, W., Xie, D., Tan, T.N.[Tie-Niu], Maybank, S.J.[Steve J.],
Learning Activity Patterns Using Fuzzy Self-Organizing Neural Network,
SMC-B(34), No. 3, June 2004, pp. 1618-1626.
IEEE Abstract. 0407
BibRef

Hu, W.M.[Wei-Ming], Xie, D.[Dan], Fu, Z.Y.[Zhou-Yu], Zeng, W.R.[Wen-Rong], Maybank, S.J.[Steve J.],
Semantic-Based Surveillance Video Retrieval,
IP(16), No. 4, April 2007, pp. 1168-1181.
IEEE DOI 0704

See also Survey on Visual Content-Based Video Indexing and Retrieval, A. BibRef

Huang, K.Q.[Kai-Qi], Wang, S., Tan, T.N.[Tie-Niu], Maybank, S.J.[Steve J.],
Human Behavior Analysis Based on a New Motion Descriptor,
CirSysVideo(19), No. 12, December 2009, pp. 1830-1840.
IEEE DOI 0912
BibRef

Oliver, N.M.[Nuria M.], Garg, A.[Ashutosh], Horvitz, E.J.[Eric J.],
Layered representations for learning and inferring office activity from multiple sensory channels,
CVIU(96), No. 2, November 2004, pp. 163-180.
Elsevier DOI 0410
BibRef

Favier, P.A.[Pierre-Alexandre], de Loor, P.[Pierre],
From Decision to Action: Intentionality, A Guide for the Specification of Intelligent Agent's Behavior,
IJIG(6), No. 1, January 2006, pp. 87-99. 0601
BibRef

Gueguen, L.[Lionel], Datcu, M.[Mihai],
Image Time-Series Data Mining Based on the Information-Bottleneck Principle,
GeoRS(45), No. 4, April 2007, pp. 827-838.
IEEE DOI 0704
BibRef

Mokhber, A.[Arash], Achard, C.[Catherine], Milgram, M.[Maurice],
Recognition of human behavior by space-time silhouette characterization,
PRL(29), No. 1, 1 January 2008, pp. 81-89.
Elsevier DOI 0711
Behavior recognition; Action recognition; Motion analysis BibRef

Achard, C.[Catherine], Qu, X.T.[Xing-Tai], Mokhber, A.[Arash], Milgram, M.[Maurice],
A novel approach for recognition of human actions with semi-global features,
MVA(19), No. 1, January 2008, pp. 27-34.
Springer DOI 0801
BibRef
Earlier:
Action Recognition with Semi-global Characteristics and Hidden Markov Models,
ACIVS07(274-284).
Springer DOI 0708
BibRef
Earlier: A3, A1, A4, A2:
Combined Classifiers for Action Recognition,
IWICPAS06(27-34).
Springer DOI 0608
BibRef
Earlier: A3, A1, A2, A4:
Action Recognition with Global Features,
CVHCI05(110).
Springer DOI 0601
BibRef

Oh, S.M.[Sang Min], Rehg, J.M.[James M.], Balch, T.[Tucker], Dellaert, F.[Frank],
Learning and Inferring Motion Patterns using Parametric Segmental Switching Linear Dynamic Systems,
IJCV(77), No. 1-3, May 2008, pp. 103-124.
Springer DOI 0803
BibRef
Earlier:
Learning and Inference in Parametric Switching Linear Dynamical Systems,
ICCV05(II: 1161-1168).
IEEE DOI 0510
BibRef
And: A1, A2, A4, Only:
Parameterized Duration Mmodeling for Switching Linear Dynamic Systems,
CVPR06(II: 1694-1700).
IEEE DOI 0606
Bee dances. for learning parameterized motions. BibRef

Xie, L., Sundaram, H., Campbell, M.,
Event Mining in Multimedia Streams,
PIEEE(96), No. 4, April 2008, pp. 623-647.
IEEE DOI 0804
BibRef

Jung, C.R., Hennemann, L., Musse, S.R.,
Event Detection Using Trajectory Clustering and 4-D Histograms,
CirSysVideo(18), No. 11, November 2008, pp. 1565-1575.
IEEE DOI 0811
BibRef

Yuan, J., Meng, J., Wu, Y., Luo, J.,
Mining Recurring Events Through Forest Growing,
CirSysVideo(18), No. 11, November 2008, pp. 1597-1607.
IEEE DOI 0811
BibRef

Lymberopoulos, D., Teixeira, T., Savvides, A.,
Macroscopic Human Behavior Interpretation Using Distributed Imager and Other Sensors,
PIEEE(96), No. 10, October 2008, pp. 1657-1677.
IEEE DOI 0811
BibRef

Chin, T.J.[Tat-Jun], Suter, D.[David],
Out-of-Sample Extrapolation of Learned Manifolds,
PAMI(30), No. 9, September 2008, pp. 1547-1556.
IEEE DOI 0808
BibRef

Chin, T.J.[Tat-Jun], Wang, L.[Liang], Schindler, K.[Konrad], Suter, D.[David],
Extrapolating Learned Manifolds for Human Activity Recognition,
ICIP07(I: 381-384).
IEEE DOI 0709
BibRef

Wang, L.[Liang], Suter, D.[David],
Recognizing Human Activities from Silhouettes: Motion Subspace and Factorial Discriminative Graphical Model,
CVPR07(1-8).
IEEE DOI 0706
BibRef

Zhou, H.[Hang], Wang, L.[Liang], Suter, D.[David],
Human action recognition by feature-reduced Gaussian process classification,
PRL(30), No. 12, 1 September 2009, pp. 1059-1066,.
Elsevier DOI 0909
BibRef
Earlier:
Human motion recognition using Gaussian Processes classification,
ICPR08(1-4).
IEEE DOI 0812
Human action recognition; Characteristic-based descriptor; Gaussian process classification; Spectral feature reduction BibRef

Wang, L.[Liang], Zhou, H.[Hang], Low, S.C.[Sui-Chien], Leckie, C.[Christopher],
Action recognition via multi-feature fusion and Gaussian process classification,
WACV09(1-6).
IEEE DOI 0912
BibRef

Wang, X.G.[Xiao-Gang], Ma, X.X.[Xiao-Xu], Grimson, W.E.L.[W. Eric L.],
Unsupervised Activity Perception in Crowded and Complicated Scenes Using Hierarchical Bayesian Models,
PAMI(31), No. 3, March 2009, pp. 539-555.
IEEE DOI 0902
BibRef
Earlier:
Unsupervised Activity Perception by Hierarchical Bayesian Models,
CVPR07(1-8).
IEEE DOI 0706
Anomaly Detection. Unsupervised learning in crowded environments (stations, shopping malls, traffic). Atomic activities modeled as distibutions over low level features. Then distributions over atomic activities. Three Bayesian Mixture models: Latent Dirichlet Allocation, Hierarchical Dirichlet Processes and dual Hierarchical Dirchlet Processes. Discover and summarize atomic activities. Sebment long video into different interactions, segment motions into different activities. Detect abnormality. Support high-level queries. BibRef

Wang, X.G.[Xiao-Gang], Ma, K.T.[Keng Teck], Ng, G.W.[Gee-Wah], Grimson, W.E.L.[W. Eric L.],
Trajectory Analysis and Semantic Region Modeling Using Nonparametric Hierarchical Bayesian Models,
IJCV(95), No. 3, December 2011, pp. 287-312.
WWW Link. 1109
BibRef
Earlier:
Trajectory analysis and semantic region modeling using a nonparametric Bayesian model,
CVPR08(1-8).
IEEE DOI 0806
BibRef
And: CSAIL-2008-015, June 2008.
WWW Link. Dual Hierarchical Dirichlet Processes (Dual-HDP) Treat trajectories as documents and observation is word in the document. Cluster trajectories, abnormal ones have few other members of the cluster.
See also Unsupervised Activity Perception in Crowded and Complicated Scenes Using Hierarchical Bayesian Models. BibRef

Wang, X.G.[Xiao-Gang], Tieu, K.[Kinh], Grimson, W.E.L.[W. Eric L.],
Correspondence-Free Activity Analysis and Scene Modeling in Multiple Camera Views,
PAMI(32), No. 1, January 2010, pp. 56-71.
IEEE DOI 0912
BibRef
Earlier:
Correspondence-free multi-camera activity analysis and scene modeling,
CVPR08(1-8).
IEEE DOI 0806
BibRef
And:
Learning Semantic Scene Models by Trajectory Analysis,
ECCV06(III: 110-123).
Springer DOI 0608
BibRef
And: CSAIL-2006-008, February 2006.
WWW Link. Segment using long-term trajectories. Multiple, synchronized, uncalibrated, stationary cameras. Motion patterns. Tracked independently. Group trajectories across views. Model paths in multiple views. Detect abnormal paths. BibRef

Brdiczka, O.[Oliver], Crowley, J.L.[James L.], Reignier, P.[Patrick],
Learning Situation Models in a Smart Home,
SMC-B(39), No. 1, February 2009, pp. 56-63.
IEEE DOI 0902
BibRef

Brdiczka, O.[Oliver], Yuen, P.C.[Pong C.], Zaidenberg, S.[Sofia], Reignier, P.[Patrick], Crowley, J.L.[James L.],
Automatic Acquisition of Context Models and its Application to Video Surveillance,
ICPR06(I: 1175-1178).
IEEE DOI 0609
BibRef

Veeraraghavan, A.[Ashok], Srinivasan, M.[Mandyam], Roy-Chowdhury, A.K.[Amit K.], Chellappa, R.[Rama],
Rate-Invariant Recognition of Humans and Their Activities,
IP(18), No. 6, June 2009, pp. 1326-1339.
IEEE DOI 0905
Model based approach to actions, capture variations due to rate of execution. BibRef

Patino, J.L.[Jose Luis], Benhadda, H., Corvee, E., Brémond, F.[Francois], Thonnat, M.[Monique],
Extraction of activity patterns on large video recordings,
IET-CV(2), No. 2, June 2008, pp. 108-128.
DOI Link 0905
BibRef

Pusiol, G.[Guido], Bremond, F.[Francois], Thonnat, M.[Monique],
Trajectory Based Activity Discovery,
AVSS10(270-277).
IEEE DOI 1009
BibRef
Earlier:
Trajectory based Primitive Events for learning and recognizing activity,
THEMIS09(1081-1088).
IEEE DOI 0910
BibRef

Thonnat, M.[Monique], Brémond, F.[Francois], Pusiol, G.[Guido], Patino, J.L.[Jose Luis],
Optimizing trajectories clustering for activity recognition,
MLMotion08(xx-yy). 0810
BibRef

Gui, L.[Laura], Thiran, J.P.[Jean-Philippe], Paragios, N.[Nikos],
Cooperative Object Segmentation and Behavior Inference in Image Sequences,
IJCV(84), No. 2, August 2009, pp. xx-yy.
Springer DOI 0906
BibRef
Earlier:
Joint Object Segmentation and Behavior Classification in Image Sequences,
CVPR07(1-8).
IEEE DOI 0706
BibRef
And:
A Variational Framework for the Simultaneous Segmentation and Object Behavior Classification of Image Sequences,
SSVM07(652-664).
Springer DOI 0705
BibRef

Saleemi, I.[Imran], Shafique, K.[Khurram], Shah, M.[Mubarak],
Probabilistic Modeling of Scene Dynamics for Applications in Visual Surveillance,
PAMI(31), No. 8, August 2009, pp. 1472-1485.
IEEE DOI 0906
Model and learn activity from static camera. Generate likely paths to improve object detection and tracking. BibRef

Veeraraghavan, H.[Harini], Papanikolopoulos, N.P.[Nikolaos P.],
Learning to Recognize Video-Based Spatiotemporal Events,
ITS(10), No. 4, December 2009, pp. 628-638.
IEEE DOI 0912
BibRef

Veeraraghavan, H.[Harini], Papanikolopoulos, N.P.[Nikolaos P.], Schrater, P.[Paul],
Learning Dynamic Event Descriptions in Image Sequences,
CVPR07(1-6).
IEEE DOI 0706
BibRef

Husz, Z.L.[Zsolt L.], Wallace, A.M.[Andrew M.], Green, P.R.[Patrick R.],
Behavioural Analysis with Movement Cluster Model for Concurrent Actions,
JIVP(2011), No. 2011, pp. xx-yy.
DOI Link 1101
BibRef
Earlier:
Human activity recognition with action primitives,
AVSBS07(330-335).
IEEE DOI 0709

See also Tracking With a Hierarchical Partitioned Particle Filter and Movement Modelling. BibRef

Faruquie, T.A.[Tanveer A.], Banerjee, S.[Subhashis], Kalra, P.K.[Prem K.],
Latent topic model-based group activity discovery,
VC(27), No. 12, December 2011, pp. 1071-1082.
WWW Link. 1112
BibRef
Earlier: A1, A3, A2:
Time based Activity Inference using Latent Dirichlet Allocation,
BMVC09(xx-yy).
PDF File. 0909
Optic flow vectors. Group activities. BibRef

Faruquie, T.A.[Tanveer A.], Banerjee, S.[Subhashis], Kalra, P.K.[Prem K.],
Unsupervised Discovery of Activities and Their Temporal Behaviour,
AVSS12(100-105).
IEEE DOI 1211
BibRef
Earlier:
Unsupervised discovery of activity correlations using latent topic models,
ICCVGIP10(25-32).
DOI Link 1111
BibRef

Choudhary, A.[Ayesha], Faruquie, T.A.[Tanveer A.], Banerjee, S.[Subhashis], Chaudhury, S.[Santanu],
Discovering Activities and Their Temporal Significance,
AVSS12(240-245).
IEEE DOI 1211
BibRef

Zhang, Z.[Zhong], Wang, C.H.[Chun-Heng], Xiao, B.H.[Bai-Hua], Zhou, W.[Wen], Liu, S.[Shuang],
Action Recognition Using Context-Constrained Linear Coding,
SPLetters(19), No. 7, July 2012, pp. 439-442.
IEEE DOI 1206
BibRef
Earlier:
Human Action Recognition with Attribute Regularization,
AVSS12(112-117).
IEEE DOI 1211
BibRef

Zhang, Z.[Zhong], Wang, C.H.[Chun-Heng], Xiao, B.H.[Bai-Hua], Zhou, W.[Wen], Liu, S.[Shuang],
Cross-View Action Recognition Using Contextual Maximum Margin Clustering,
CirSysVideo(24), No. 10, October 2014, pp. 1663-1668.
IEEE DOI 1411
gesture recognition BibRef

Zhou, W.[Wen], Wang, C.H.[Chun-Heng], Xiao, B.H.[Bai-Hua], Zhang, Z.[Zhong], Shao, Y.X.[Yun-Xue],
Modeling Interactions between Low-Level and High-Level Features for Human Action Recognition,
IEICE(E96-D), No. 12, December 2013, pp. 2896-2899.
WWW Link. 1312
BibRef

Zhou, W.[Wen], Wang, C.H.[Chun-Heng], Xiao, B.H.[Bai-Hua], Zhang, Z.[Zhong],
Human action recognition using weighted pooling,
IET-CV(8), No. 6, 2014, pp. 579-587.
DOI Link 1502
gesture recognition
See also Cross-view face recognition via structured dictionary based domain shift. BibRef

Zhang, Z.[Zhong], Wang, C.H.[Chun-Heng], Xiao, B.H.[Bai-Hua], Zhou, W.[Wen], Liu, S.[Shuang],
Contextual Fisher kernels for human action recognition,
ICPR12(437-440).
WWW Link. 1302
BibRef

Zhou, W.[Wen], Wang, C.H.[Chun-Heng], Xiao, B.H.[Bai-Hua], Zhang, Z.[Zhong], Ma, L.[Long],
Human action recognition by bagging data dependent representation,
ICPR12(3120-3123).
WWW Link. 1302
BibRef

Zhang, Z.[Zhong], Wang, C.H.[Chun-Heng], Xiao, B.H.[Bai-Hua], Zhou, W.[Wen], Liu, S.[Shuang], Shi, C.Z.[Cun-Zhao],
Cross-View Action Recognition via a Continuous Virtual Path,
CVPR13(2690-2697)
IEEE DOI 1309
BibRef

Zhou, W.[Wen], Wang, C.H.[Chun-Heng], Xiao, B.H.[Bai-Hua], Zhang, Z.[Zhong],
Action recognition via structured codebook construction,
SP:IC(29), No. 4, 2014, pp. 546-555.
Elsevier DOI 1404
Action recognition BibRef

An, G.Y.[Gao-Yun], Zheng, Z.X.[Zhen-Xing], Wu, D.P.[Da-Peng], Zhou, W.[Wen],
Deep spectral feature pyramid in the frequency domain for long-term action recognition,
JVCIR(64), 2019, pp. 102650.
Elsevier DOI 1911
Action recognition, Deep learning, Spectral feature, Video classification BibRef

Duan, L.X.[Li-Xin], Xu, D.[Dong], Tsang, I.W.H.[Ivor Wai-Hung], Luo, J.B.[Jie-Bo],
Visual Event Recognition in Videos by Learning from Web Data,
PAMI(34), No. 9, September 2012, pp. 1667-1680.
IEEE DOI 1208
BibRef
Earlier: CVPR10(1959-1966).
IEEE DOI Video of talk:
WWW Link. 1006
Award, CVPR, Student. BibRef

Chen, L.[Lin], Duan, L.X.[Li-Xin], Xu, D.[Dong],
Event Recognition in Videos by Learning from Heterogeneous Web Sources,
CVPR13(2666-2673)
IEEE DOI 1309
Domain Adaptation; Event Recognition BibRef

Wu, X.X.[Xin-Xiao], Wang, R.Q.[Rui-Qi], Hou, J.Y.[Jing-Yi], Lin, H.X.[Han-Xi], Luo, J.B.[Jie-Bo],
Spatial-Temporal Relation Reasoning for Action Prediction in Videos,
IJCV(129), No. 5, May 2021, pp. 1484-1505.
Springer DOI 2105
BibRef

Wu, X.X.[Xin-Xiao], Xu, D.[Dong], Duan, L.X.[Li-Xin], Luo, J.B.[Jie-Bo],
Action recognition using context and appearance distribution features,
CVPR11(489-496).
IEEE DOI 1106
BibRef

Yi, S.[Sheng], Krim, H.[Hamid], Norris, L.K.[Larry K.],
Human Activity as a Manifold-Valued Random Process,
IP(21), No. 8, August 2012, pp. 3416-3428.
IEEE DOI 1208
BibRef
Earlier:
A invertible dimension reduction of curves on a manifold,
ITCVPR11(1378-1385).
IEEE DOI 1201
BibRef
And:
Human Activity Modeling as Brownian Motion on Shape Manifold,
SSVM11(628-639).
Springer DOI 1201
BibRef
Earlier:
Human Activity Modeling On Shape Manifold,
3DOR11(105-112)
DOI Link 1301
BibRef

Yi, S.[Sheng], Krim, H.[Hamid],
Subspace Learning of Dynamics on a Shape Manifold: A Generative Modeling Approach,
IP(23), No. 11, November 2014, pp. 4907-4919.
IEEE DOI 1410
Equations BibRef

Bian, X.[Xiao], Krim, H.[Hamid],
Optimal Operator Space Pursuit: A Framework for Video Sequence Data Analysis,
ACCV12(II:760-769).
Springer DOI 1304
video based human activity classification BibRef

Castrodad, A.[Alexey], Sapiro, G.[Guillermo],
Sparse Modeling of Human Actions from Motion Imagery,
IJCV(100), No. 1, October 2012, pp. 1-15.
WWW Link. 1208
BibRef

Junejo, I.N.[Imran N.], Al Aghbari, Z.[Zaher],
Using SAX representation for human action recognition,
JVCIR(23), No. 6, August 2012, pp. 853-861.
Elsevier DOI 1208
Action recognition; Pattern recognition; Image understanding; SAX representation; Data mining; Intelligent systems; Video surveillance BibRef

Junejo, I.N.[Imran N.], Junejo, K.N.[Khurrum Nazir], Al Aghbari, Z.[Zaher],
Silhouette-based human action recognition using SAX-Shapes,
VC(30), No. 3, March 2014, pp. 259-269.
WWW Link. 1403
BibRef

San Miguel, J.C.[Juan C.], Martínez, J.M.[José M.],
A semantic-based probabilistic approach for real-time video event recognition,
CVIU(116), No. 9, September 2012, pp. 937-952.
Elsevier DOI 1208
Video event detection; Semantic video analysis; Bayes Network; Petri Net; Low-level uncertainty
See also Rejection based multipath reconstruction for background estimation in video sequences with stationary objects. BibRef

San Miguel, J.C.[Juan C.], Martínez, J.M.[José M.],
A semantic-guided and self-configurable framework for video analysis,
MVA(24), No. 3, April 2013, pp. 493-512.
WWW Link. 1303

See also Standalone evaluation of deterministic video tracking. BibRef

San Miguel, J.C.[Juan Carlos], Martinez, J.M.[José M.], Garcia, Á.[Álvaro],
An Ontology for Event Detection and its Application in Surveillance Video,
AVSBS09(220-225).
IEEE DOI 0909
BibRef

Weinshall, D.[Daphna], Zweig, A.[Alon], Hermansky, H.[Hynek], Kombrink, S.[Stefan], Ohl, F.W.[Frank W.], Anemüller, J.[Jörn], Bach, J.H.[Jörg-Hendrik], Van Gool, L.J.[Luc J.], Nater, F.[Fabian], Pajdla, T.[Tomas], Havlena, M.[Michal], Pavel, M.[Misha],
Beyond Novelty Detection: Incongruent Events, When General and Specific Classifiers Disagree,
PAMI(34), No. 10, October 2012, pp. 1886-1901.
IEEE DOI 1208
Unexpected stimuli with machine learning. Label hierarchy. Find events that do not fit. BibRef

Noceti, N.[Nicoletta], Odone, F.[Francesca],
Learning common behaviors from large sets of unlabeled temporal series,
IVC(30), No. 11, November 2012, pp. 875-895.
Elsevier DOI 1211
BibRef
And:
Unsupervised Video Surveillance,
VS10(84-93).
Springer DOI 1109
Behavior analysis; Temporal series clustering; Anomaly detection; Unsupervised learning BibRef

Noceti, N.[Nicoletta], Caputo, B.[Barbara], Castellini, C.[Claudio], Baldassarre, L.[Luca], Barla, A.[Annalisa], Rosasco, L.[Lorenzo], Odone, F.[Francesca], Sandini, G.[Giulio],
Towards a Theoretical Framework for Learning Multi-modal Patterns for Embodied Agents,
CIAP09(239-248).
Springer DOI 0909
BibRef

Noceti, N.[Nicoletta], Santoro, M.[Matteo], Odone, F.[Francesca],
Unsupervised learning of behavioural patterns for video-surveillance,
MLMotion08(xx-yy). 0810
BibRef

Noceti, N.[Nicoletta], Balduzzi, L.[Luigi],
What Epipolar Geometry Can Do for Video-Surveillance,
CIAP13(I:442-451).
Springer DOI 1311
matching moving objects between multiple views BibRef

Fu, W.[Wei], Wang, J.Q.[Jin-Qiao], Lu, H.Q.[Han-Qing], Ma, S.D.[Song-De],
Dynamic scene understanding by improved sparse topical coding,
PR(46), No. 7, July 2013, pp. 1841-1850.
Elsevier DOI 1303
Motion patterns; Sparse topical coding; Scene understanding BibRef

Fu, W.[Wei], Wang, J.Q.[Jin-Qiao], Zhao, C.Y.[Chao-Yang], Lu, H.Q.[Han-Qing], Ma, S.D.[Song-De],
Object-centered narratives for video surveillance,
ICIP12(29-32).
IEEE DOI 1302
BibRef

Roggen, D.[Daniel], Töster, G.[Gerhard], Lukowicz, P., Ferscha, A., Millán, J.D.[José Del_R.], Chavarriaga, R.[Ricardo],
Opportunistic Human Activity and Context Recognition,
Computer(46), No. 2, February 2013, pp. 36-45.
IEEE DOI 1303
BibRef

Ohnishi, N.[Naoya], Imiya, A.[Atsushi],
Appearance-based navigation and homing for autonomous mobile robot,
IVC(31), No. 6-7, June-July 2013, pp. 511-532.
Elsevier DOI 1306
BibRef
Earlier:
Visual Navigation of Mobile Robot Using Optical Flow and Visual Potential Field,
RobVis08(412-426).
Springer DOI 0802
BibRef
Earlier:
Combination of supervised and unsupervised methods for navigation path mining,
MLMotion08(xx-yy). 0810
BibRef
Earlier:
Corridor Navigation and Obstacle Avoidance using Visual Potential for Mobile Robot,
CRV07(131-138).
IEEE DOI 0705
Visual navigation; Visual homing; Nonholonomic mobile robot; Visual potential; Optical flow; Dominant plane BibRef

Ohnishi, N.[Naoya], Imiya, A.[Atsushi],
Dominant plane detection from optical flow for robot navigation,
PRL(27), No. 9, July 2006, pp. 1009-1021.
Elsevier DOI 0605
Dominant plane detection; Affine transformation BibRef

Ohnishi, N.[Naoya], Imiya, A.[Atsushi],
Independent Component Analysis of Layer Optical Flow and Its Application,
BVAI07(171-180).
Springer DOI 0710
BibRef
And:
Model-Based Plane-Segmentation Using Optical Flow and Dominant Plane,
MIRAGE07(295-306).
Springer DOI 0703
BibRef

Imiya, A.[Atsushi], Yamada, D.[Daisuke],
Voting Method for Stable Range Optical Flow Computation,
PSIVT06(332-341).
Springer DOI 0612
BibRef

Trinh, V.C., Gonzalez, A.J.,
Discovering Contexts from Observed Human Performance,
HMS(43), No. 4, 2013, pp. 359-370.
IEEE DOI 1307
behavioural sciences computing BibRef

Zhen, X.T.[Xian-Tong], Shao, L.[Ling], Tao, D.C.[Da-Cheng], Li, X.L.[Xue-Long],
Embedding Motion and Structure Features for Action Recognition,
CirSysVideo(23), No. 7, 2013, pp. 1182-1190.
IEEE DOI 1307
Gabor filters
See also Learning Discriminative Key Poses for Action Recognition. BibRef

Shao, L.[Ling], Jones, S., Li, X.L.[Xue-Long],
Efficient Search and Localization of Human Actions in Video Databases,
CirSysVideo(24), No. 3, March 2014, pp. 504-512.
IEEE DOI 1404
image motion analysis BibRef

Aztiria, A., Augusto, J.C., Basagoiti, R., Izaguirre, A., Cook, D.J.,
Learning Frequent Behaviors of the Users in Intelligent Environments,
SMCS(43), No. 6, 2013, pp. 1265-1278.
IEEE DOI 1311
Ambient intelligence BibRef

Ben Aoun, N.[Najib], Mejdoub, M.[Mahmoud], Ben Amar, C.[Chokri],
Graph-based approach for human action recognition using spatio-temporal features,
JVCIR(25), No. 2, 2014, pp. 329-338.
Elsevier DOI 1402
Human action recognition BibRef

Sekma, M.[Manel], Mejdoub, M.[Mahmoud], Ben Amar, C.[Chokri],
Human action recognition based on multi-layer Fisher vector encoding method,
PRL(65), No. 1, 2015, pp. 37-43.
Elsevier DOI 1511
BibRef
Earlier:
Human Action Recognition Using Temporal Segmentation and Accordion Representation,
CAIP13(II:563-570).
Springer DOI 1311
Human action recognition BibRef

Sekma, M.[Manel], Mejdoub, M.[Mahmoud], Ben Amar, C.[Chokri],
Bag of Graphs with Geometric Relationships Among Trajectories for Better Human Action Recognition,
CIAP15(I:85-96).
Springer DOI 1511
BibRef

Ben Aoun, N.[Najib], Elghazel, H.[Haytham], Hacid, M.S.[Mohand-Said], Ben Amar, C.[Chokri],
Graph Aggregation Based Image Modeling and Indexing for Video Annotation,
CAIP11(II: 324-331).
Springer DOI 1109
BibRef

Wali, A.[Ali], Ben Aoun, N.[Najib], Karray, H.[Hichem], Ben Amar, C.[Chokri], Alimi, A.M.[Adel M.],
A New System for Event Detection from Video Surveillance Sequences,
ACIVS10(II: 110-120).
Springer DOI 1012
BibRef

Wali, A., Alimi, A.M.,
Incremental Learning Approach for Events Detection from Large Video Dataset,
AVSS10(555-560).
IEEE DOI 1009
BibRef

Burghouts, G.J., Schutte, K., Bouma, H., den Hollander, R.J.M.,
Selection of negative samples and two-stage combination of multiple features for action detection in thousands of videos,
MVA(25), No. 1, January 2014, pp. 85-98.
Springer DOI 1402
BibRef

Cuzzolin, F.[Fabio], Sapienza, M.[Michael],
Learning Pullback HMM Distances,
PAMI(36), No. 7, July 2014, pp. 1483-1489.
IEEE DOI 1407
Covariance matrices BibRef

Sapienza, M.[Michael], Cuzzolin, F.[Fabio], Torr, P.H.S.[Philip H.S.],
Learning Discriminative Space-Time Action Parts from Weakly Labelled Videos,
IJCV(110), No. 1, October 2014, pp. 30-47.
Springer DOI 1410
BibRef
Earlier:
Learning discriminative space-time actions from weakly labelled videos,
BMVC12(123).
DOI Link 1301
BibRef

Singh, G.[Gurkirt], Saha, S.[Suman], Cuzzolin, F.[Fabio],
TraMNet: Transition Matrix Network for Efficient Action Tube Proposals,
ACCV18(VI:420-437).
Springer DOI 1906
BibRef

Singh, G.[Gurkirt], Cuzzolin, F.[Fabio],
Recurrent Convolutions for Causal 3D CNNs,
HVU19(1456-1465)
IEEE DOI 2004
convolutional neural nets, image reconstruction, image representation, image resolution, image sequences, Causal reasoning BibRef

Saha, S.[Suman], Singh, G.[Gurkirt], Sapienza, M.[Michael], Torr, P.H.S.[Philip H.S.], Cuzzolin, F.[Fabio],
Deep Learning for Detecting Multiple Space-Time Action Tubes in Videos,
BMVC16(xx-yy).
HTML Version. 1805
BibRef

Jetley, S.[Saumya], Cuzzolin, F.[Fabio],
3D Activity Recognition Using Motion History and Binary Shape Templates,
Gait14(I: 129-144).
Springer DOI 1504
BibRef

Fernando, B.[Basura], Gould, S.[Stephen],
Discriminatively Learned Hierarchical Rank Pooling Networks,
IJCV(124), No. 3, September 2017, pp. 335-355.
Springer DOI 1708
BibRef

Fernando, B.[Basura], Anderson, P.[Peter], Hutter, M.[Marcus], Gould, S.[Stephen],
Discriminative Hierarchical Rank Pooling for Activity Recognition,
CVPR16(1924-1932)
IEEE DOI 1612
BibRef

de Rosa, R.[Rocco], Gori, I.[Ilaria], Cuzzolin, F.[Fabio], Cesa-Bianchi, N.[Nicolò],
Active Incremental Recognition of Human Activities in a Streaming Context,
PRL(99), No. 1, 2017, pp. 48-56.
Elsevier DOI 1710
BibRef
Earlier: A1, A4, A2, A3:
Online Action Recognition via Nonparametric Incremental Learning,
BMVC14(xx-yy).
HTML Version. 1410
Activity recognition BibRef

Mazloom, M., Gavves, E.[Efstratios], Snoek, C.G.M.[Cees G.M.],
Conceptlets: Selective Semantics for Classifying Video Events,
MultMed(16), No. 8, December 2014, pp. 2214-2228.
IEEE DOI 1502
feature extraction BibRef

Hussein, N., Gavves, E.[Efstratios], Smeulders, A.W.M.[Arnold W.M.],
Unified Embedding and Metric Learning for Zero-Exemplar Event Detection,
CVPR17(2087-2096)
IEEE DOI 1711
Detectors, Event detection, Measurement, Semantics, Videos, Visualization BibRef

Mazloom, M., Li, X., Snoek, C.G.M.[Cees G. M.],
TagBook: A Semantic Video Representation Without Supervision for Event Detection,
MultMed(18), No. 7, July 2016, pp. 1378-1388.
IEEE DOI 1608
image representation BibRef

Li, Z.Y.[Zhen-Yang], Gavrilyuk, K.[Kirill], Gavves, E.[Efstratios], Jain, M.[Mihir], Snoek, C.G.M.[Cees G.M.],
VideoLSTM convolves, attends and flows for action recognition,
CVIU(166), No. 1, 2018, pp. 41-50.
Elsevier DOI 1712
Action recognition BibRef

Kooij, J.F.P.[Julian F.P.], Englebienne, G.[Gwenn], Gavrila, D.M.[Dariu M.],
Identifying multiple objects from their appearance in inaccurate detections,
CVIU(136), No. 1, 2015, pp. 103-116.
Elsevier DOI 1506
BibRef
Earlier:
A Non-parametric Hierarchical Model to Discover Behavior Dynamics from Tracks,
ECCV12(VI: 270-283).
Springer DOI 1210
Object recognition. Keep track of multiple objects in video. BibRef

Kooij, J.F.P.[Julian F.P.], Englebienne, G.[Gwenn], Gavrila, D.M.[Dariu M.],
Mixture of Switching Linear Dynamics to Discover Behavior Patterns in Object Tracks,
PAMI(38), No. 2, February 2016, pp. 322-334.
IEEE DOI 1601
Dynamics BibRef

Elloumi, S., Cosar, S., Pusiol, G., Bremond, F., Thonnat, M.,
Unsupervised discovery of human activities from long-time videos,
IET-CV(9), No. 4, 2015, pp. 522-530.
DOI Link 1509
feature extraction BibRef

Murthy, O.V.R.[O.V. Ramana], Goecke, R.[Roland],
Ordered trajectories for human action recognition with large number of classes,
IVC(42), No. 1, 2015, pp. 22-34.
Elsevier DOI 1509
Action recognition BibRef
Earlier:
The Influence of Temporal Information on Human Action Recognition with Large Number of Classes,
DICTA14(1-8)
IEEE DOI 1502
BibRef
Earlier:
Ordered Trajectories for Large Scale Human Action Recognition,
THUMOS13(412-419)
IEEE DOI 1403
object recognition. image representation
See also Injury Mechanism Classification in Soccer Videos. BibRef

Fanaeepour, M.[Maryam], Kulik, L.[Lars], Tanin, E.[Egemen], Rubinstein, B.I.P.[Benjamin I.P.],
The CASE histogram: Privacy-aware processing of trajectory data using aggregates,
GeoInfo(19), No. 4, October 2015, pp. 747-798.
IEEE DOI 1511
Location based services, process the data, but hide identity. BibRef

Zhang, G.[Guopeng], Piccardi, M.,
Structural SVM with Partial Ranking for Activity Segmentation and Classification,
SPLetters(22), No. 12, December 2015, pp. 2344-2348.
IEEE DOI 1512
image classification BibRef

Wang, H.[Heng], Oneata, D.[Dan], Verbeek, J.[Jakob], Schmid, C.[Cordelia],
A Robust and Efficient Video Representation for Action Recognition,
IJCV(119), No. 3, September 2016, pp. 219-238.
Springer DOI 1608
BibRef
Earlier: A2, A3, A4, Only:
Efficient Action Localization with Approximately Normalized Fisher Vectors,
CVPR14(2545-2552)
IEEE DOI 1409
BibRef
Earlier: A2, A3, A4, Only:
Action and Event Recognition with Fisher Vectors on a Compact Feature Set,
ICCV13(1817-1824)
IEEE DOI 1403
Fisher vectors
See also Segmentation Driven Object Detection with Fisher Vectors. BibRef

Rohrbach, M.[Marcus], Rohrbach, A.[Anna], Regneri, M.[Michaela], Amin, S.[Sikandar], Andriluka, M.[Mykhaylo], Pinkal, M.[Manfred], Schiele, B.[Bernt],
Recognizing Fine-Grained and Composite Activities Using Hand-Centric Features and Script Data,
IJCV(119), No. 3, September 2016, pp. 346-373.
Springer DOI 1608
BibRef
Earlier: A1, A3, A5, A4, A6, A7, Only:
Script Data for Attribute-Based Recognition of Composite Activities,
ECCV12(I: 144-157).
Springer DOI 1210
BibRef

Peng, X.J.[Xiao-Jiang], Wang, L.M.[Li-Min], Wang, X.X.[Xing-Xing], Qiao, Y.[Yu],
Bag of visual words and fusion methods for action recognition: Comprehensive study and good practice,
CVIU(150), No. 1, 2016, pp. 109-125.
Elsevier DOI 1608
Action recognition BibRef

Peng, X.J.[Xiao-Jiang], Wang, L.M.[Li-Min], Cai, Z.W.[Zhuo-Wei], Qiao, Y.[Yu],
Action and Gesture Temporal Spotting with Super Vector Representation,
ChaLearn14(518-527).
Springer DOI 1504
BibRef

Peng, X.J.[Xiao-Jiang], Wang, L.M.[Li-Min], Qiao, Y.[Yu], Peng, Q.A.[Qi-Ang],
A Joint Evaluation of Dictionary Learning and Feature Encoding for Action Recognition,
ICPR14(2607-2612)
IEEE DOI 1412
BibRef
And:
Boosting VLAD with Supervised Dictionary Learning and High-Order Statistics,
ECCV14(III: 660-674).
Springer DOI 1408
Accuracy BibRef

Cai, Z.W.[Zhuo-Wei], Wang, L.M.[Li-Min], Peng, X.J.[Xiao-Jiang], Qiao, Y.[Yu],
Multi-view Super Vector for Action Recognition,
CVPR14(596-603)
IEEE DOI 1409
action recognition BibRef

Liu, J.Y.[Jing-Yu], Huang, Y.Z.[Yong-Zhen], Peng, X.J.[Xiao-Jiang], Wang, L.[Liang],
Multi-view descriptor mining via codeword net for action recognition,
ICIP15(793-797)
IEEE DOI 1512
BoVW; action recognition; codeword net; multi-view descriptor mining BibRef

Zhen, X.T.[Xian-Tong], Shao, L.[Ling], Maybank, S.J.[Stephen J.], Chellappa, R.[Rama],
Handcrafted vs. learned representations for human action recognition,
IVC(55, Part 2), No. 1, 2016, pp. 39-41.
Elsevier DOI 1612
BibRef

Zhu, F.[Fan], Shao, L.[Ling], Xie, J.[Jin], Fang, Y.[Yi],
From handcrafted to learned representations for human action recognition: A survey,
IVC(55, Part 2), No. 1, 2016, pp. 42-52.
Elsevier DOI 1612
Human action recognition BibRef

Li, S.[Shuohao], Zhang, J.[Jun], Guo, Q.A.[Qi-Ang], Lei, J.[Jun], Tu, D.[Dan],
Generating image descriptions with multidirectional 2D long short-term memory,
IET-CV(11), No. 1, February 2017, pp. 104-111.
DOI Link 1703
BibRef
Earlier:
Generating video description with Long-Short Term Memory,
ICIVC16(73-78)
IEEE DOI 1610
computer vision BibRef

Lei, J.[Jun], Li, G., Li, S.[Shuohao], Tu, D.[Dan], Guo, Q.A.[Qi-Ang],
Continuous action recognition based on hybrid CNN-LDCRF model,
ICIVC16(63-69)
IEEE DOI 1610
feature selection BibRef

Li, Q.[Qing], Qiu, Z.F.[Zhao-Fan], Yao, T.[Ting], Mei, T.[Tao], Rui, Y.[Yong], Luo, J.B.[Jie-Bo],
Learning hierarchical video representation for action recognition,
MultInfoRetr(6), No. 1, March 2017, pp. 85-98.
Springer DOI 1704
BibRef

Gilbert, A.[Andrew], Bowden, R.[Richard],
Image and video mining through online learning,
CVIU(158), No. 1, 2017, pp. 72-84.
Elsevier DOI 1704
BibRef
Earlier:
iGroup: Weakly supervised image and video grouping,
ICCV11(2166-2173).
IEEE DOI 1201
BibRef
And:
Push and Pull: Iterative grouping of media,
BMVC11(xx-yy).
HTML Version. 1110
Action recognition. sub activities withing the scene. BibRef

Ahmad, K.[Kashif], Conci, N.[Nicola], de Natale, F.G.B.[Francesco G.B.],
A saliency-based approach to event recognition,
SP:IC(60), No. 1, 2018, pp. 42-51.
Elsevier DOI 1712
Event recognition BibRef

Hu, C.Y.[Chun-Yu], Chen, Y.Q.[Yi-Qiang], Hu, L.S.[Li-Sha], Peng, X.H.[Xiao-Hui],
A novel random forests based class incremental learning method for activity recognition,
PR(78), 2018, pp. 277-290.
Elsevier DOI 1804
Class incremental learning, Activity recognition, Random forests BibRef

Plötz, T., Guan, Y.,
Deep Learning for Human Activity Recognition in Mobile Computing,
Computer(51), No. 5, May 2018, pp. 50-59.
IEEE DOI 1806
Analytical models, Computational modeling, Data mining, Data models, Feature extraction, Machine learning, pattern recognition BibRef

Dong, X.J.[Xiang-Jun], Gong, Y.S.[Yong-Shun], Cao, L.B.[Long-Bing],
F-NSP+: A fast negative sequential patterns mining method with self-adaptive data storage,
PR(84), 2018, pp. 13-27.
Elsevier DOI 1809
Nonoccurring behavior analysis, Sequential patterns, Negative sequential patterns, Bitmap BibRef

Jacoby, A.R., Pattichis, M.S., Celedón-Pattichis, S., López Leiva, C.,
Context-Sensitive Human Activity Classification in Collaborative Learning Environments,
Southwest18(1-4)
IEEE DOI 1809
Writing, Keyboards, Feature extraction, Image color analysis, Neural networks, Collaborative work, Monitoring, context-based methods BibRef

Chen, Y., Wang, J., Bai, Y., Castañón, G., Saligrama, V.,
Probabilistic Semantic Retrieval for Surveillance Videos With Activity Graphs,
MultMed(21), No. 3, March 2019, pp. 704-716.
IEEE DOI 1903
graph theory, image retrieval, information retrieval, learning (artificial intelligence), object detection, subgraph matching BibRef

Xu, H.J.[Hui-Juan], Das, A.[Abir], Saenko, K.[Kate],
Two-Stream Region Convolutional 3D Network for Temporal Activity Detection,
PAMI(41), No. 10, October 2019, pp. 2319-2332.
IEEE DOI 1909
BibRef
Earlier:
R-C3D: Region Convolutional 3D Network for Temporal Activity Detection,
ICCV17(5794-5803)
IEEE DOI 1802
Proposals, Feature extraction, Streaming media, Training, hard mining. convolution, image classification, image motion analysis, learning (artificial intelligence), BibRef

Liao, Z.K.[Zhong-Ke], Hu, H.F.[Hai-Feng], Zhang, J.X.[Jun-Xuan], Yin, C.[Chang],
Residual attention unit for action recognition,
CVIU(189), 2019, pp. 102821.
Elsevier DOI 1911
Action recognition, Residual learning, Attention, Background motion BibRef

Xu, W., Miao, Z., Yu, J., Ji, Q.,
Deep Reinforcement Learning for Weak Human Activity Localization,
IP(29), 2020, pp. 1522-1535.
IEEE DOI 1911
Reinforcement learning, Proposals, Video sequences, Training, Feature extraction, Task analysis, Computational modeling, weak supervision BibRef

Feichtenhofer, C.[Christoph], Pinz, A.[Axel], Wildes, R.P.[Richard P.], Zisserman, A.[Andrew],
Deep Insights into Convolutional Networks for Video Recognition,
IJCV(128), No. 2, February 2020, pp. 420-437.
Springer DOI 2002
BibRef
Earlier: A2, A4, A3, A1:
What have We Learned from Deep Representations for Action Recognition?,
CVPR18(7844-7853)
IEEE DOI 1812
Visualization, Spatiotemporal phenomena, Optimization, Computational modeling, Optical imaging, Optical network units, Generators BibRef

Han, X.X.[Xin-Xin], Ye, J.[Jian], Luo, J.[Jia], Zhou, H.Y.[Hai-Ying],
The Effect of Axis-Wise Triaxial Acceleration Data Fusion in CNN-Based Human Activity Recognition,
IEICE(E103-D), No. 4, April 2020, pp. 813-824.
WWW Link. 2004
BibRef

Tang, Y., Wei, Y., Yu, X., Lu, J., Zhou, J.,
Graph Interaction Networks for Relation Transfer in Human Activity Videos,
CirSysVideo(30), No. 9, September 2020, pp. 2872-2886.
IEEE DOI 2009
Videos, Task analysis, Activity recognition, Feature extraction, Knowledge transfer, Knowledge engineering, Convolution, transfer learning BibRef

Wu, H.N.[Huai-Ning],
Online Learning Human Behavior for a Class of Human-in-the-Loop Systems via Adaptive Inverse Optimal Control,
HMS(52), No. 5, October 2022, pp. 1004-1014.
IEEE DOI 2209
Optimal control, Adaptive systems, Cost function, Task analysis, Linear matrix inequalities, Symmetric matrices, Trajectory, linear quadratic regulator (LQR) BibRef

Li, Y.L.[Yong-Lu], Liu, X.P.[Xin-Peng], Wu, X.Q.[Xiao-Qian], Li, Y.Z.[Yi-Zhuo], Qiu, Z.Y.[Zuo-Yu], Xu, L.[Liang], Xu, Y.[Yue], Fang, H.S.[Hao-Shu], Lu, C.W.[Ce-Wu],
HAKE: A Knowledge Engine Foundation for Human Activity Understanding,
PAMI(45), No. 7, July 2023, pp. 8494-8506.
IEEE DOI 2306
Cognition, Semantics, Visualization, Object recognition, Knowledge based systems, Task analysis, Programming, neural-symbolic reasoning BibRef

Hu, S.[Sha], Gong, Y.[Yu], Mori, G.[Greg],
Embodied Human Activity Recognition,
WACV24(6433-6443)
IEEE DOI 2404
Reinforcement learning, Behavioral sciences, Human activity recognition, Task analysis, Algorithms, Video recognition and understanding BibRef

Panev, S.[Stanislav], Kim, E.[Emily], Namburu, S.A.S.[Sai Abhishek Si], Nikolova, D.[Desislava], de Melo, C.[Celso], de la Torre, F.[Fernando], Hodgins, J.K.[Jessica K.],
Exploring the Impact of Rendering Method and Motion Quality on Model Performance when Using Multi-view Synthetic Data for Action Recognition,
WACV24(4580-4590)
IEEE DOI Code:
WWW Link. 2404
Training, Computational modeling, Training data, Rendering (computer graphics), Cameras, Data models, Robustness BibRef

Zhao, M.J.[Ming-Jun], Yu, Y.K.[Ya-Kun], Wang, X.L.[Xiao-Li], Yang, L.[Lei], Niu, D.[Di],
Search-Map-Search: A Frame Selection Paradigm for Action Recognition,
CVPR23(10627-10636)
IEEE DOI 2309
select frames to ues for training. BibRef

Agrawal, T.[Tanay], Balazia, M.[Michal], Müller, P.[Philipp], Brémond, F.[François],
Multimodal Vision Transformers with Forced Attention for Behavior Analysis,
WACV23(3381-3391)
IEEE DOI 2302
Analytical models, Visualization, Face recognition, Memory management, Transformers, Feature extraction, Biomedical/healthcare/medicine BibRef

Li, S.W.[Sheng-Wang], Yu, P.S.[Peng-Shuai], Xu, Y.F.[Yun-Feng], Zhang, J.W.[Jing-Wei],
A Review of Research on Human Behavior Recognition Methods Based on Deep Learning,
ICRVC22(108-112)
IEEE DOI 2301
Deep learning, Image recognition, Face recognition, Surveillance, Virtual reality, Smart homes, Human behavior recognition, human behavior recognition dataset BibRef

Kakamu, Y.[Yoshiki], Hotta, K.[Kazuhiro],
Predicting Human Behavior Using 3D Loop ResNet,
ICPR22(3259-3264)
IEEE DOI 2212
Medical services, Feature extraction, Behavioral sciences, Task analysis, Action recognition BibRef

Tran, H.[Hung], Le, V.[Vuong], Venkatesh, S.[Svetha], Tran, T.[Truyen],
Persistent-Transient Duality in Human Behavior Modeling,
Precognition22(2527-2530)
IEEE DOI 2210
Computational modeling, Dynamics, Neural networks, Switches, Predictive models BibRef

Xu, Y.H.[Ying-Hao], Wei, F.Y.[Fang-Yun], Sun, X.[Xiao], Yang, C.[Ceyuan], Shen, Y.J.[Yu-Jun], Dai, B.[Bo], Zhou, B.[Bolei], Lin, S.[Stephen],
Cross-Model Pseudo-Labeling for Semi-Supervised Action Recognition,
CVPR22(2949-2958)
IEEE DOI 2210
Training, Protocols, Costs, Predictive models, Semisupervised learning, Data models, Pattern recognition, Self- semi- meta- unsupervised learning BibRef

Duan, H.D.[Hao-Dong], Zhao, N.X.[Nan-Xuan], Chen, K.[Kai], Lin, D.[Dahua],
TransRank: Self-supervised Video Representation Learning via Ranking-based Transformation Recognition,
CVPR22(2990-3000)
IEEE DOI 2210
Representation learning, Codes, Self-supervised learning, Pattern recognition, Noise measurement, Task analysis, Self- semi- meta- unsupervised learning BibRef

Jin, Y.[Yang], Zhu, L.C.[Lin-Chao], Mu, Y.D.[Ya-Dong],
Complex Video Action Reasoning via Learnable Markov Logic Network,
CVPR22(3232-3241)
IEEE DOI 2210
Training, Location awareness, Visualization, Reinforcement learning, Markov processes, Predictive models, Explainable computer vision BibRef

Xiao, J.F.[Jun-Fei], Jing, L.L.[Long-Long], Zhang, L.[Lin], He, J.[Ju], She, Q.[Qi], Zhou, Z.W.[Zong-Wei], Yuille, A.L.[Alan L.], Li, Y.W.[Ying-Wei],
Learning from Temporal Gradient for Semi-supervised Action Recognition,
CVPR22(3242-3252)
IEEE DOI 2210
Deep learning, Codes, Neural networks, Dynamics, Semisupervised learning, Feature extraction, Video analysis and understanding BibRef

Chen, H., Chirikjian, G.S.,
Curvature: A signature for Action Recognition in Video Sequences,
Diff-CVML20(3752-3759)
IEEE DOI 2008
Video sequences, Vegetation, Forestry, Data models, Feature extraction, Calculus, Histograms BibRef

Sun, J., Jiang, Q., Lu, C.,
Recursive Social Behavior Graph for Trajectory Prediction,
CVPR20(657-666)
IEEE DOI 2008
Trajectory, Task analysis, Force, Recurrent neural networks, Forecasting, Predictive models BibRef

Price, W., Damen, D.[Dima],
Retro-Actions: Learning 'Close' by Time-Reversing 'Open' Videos,
MDALC19(1371-1380)
IEEE DOI 2004
learning (artificial intelligence), object recognition, video signal processing, Something-Something dataset, arrow of time BibRef

Abu Farha, Y., Gall, J.,
Uncertainty-Aware Anticipation of Activities,
HBU19(1197-1204)
IEEE DOI 2004
learning (artificial intelligence), statistical distributions, multimodal future activities, predicted time horizon, Long term Prediction BibRef

Wang, X.H.[Xiong-Hui], Hu, J.F.[Jian-Fang], Lai, J.H.[Jian-Huang], Zhang, J.G.[Jian-Guo], Zheng, W.S.[Wei-Shi],
Progressive Teacher-Student Learning for Early Action Prediction,
CVPR19(3551-3560).
IEEE DOI 2002
BibRef

Mehrasa, N.[Nazanin], Jyothi, A.A.[Akash Abdu], Durand, T.[Thibaut], He, J.W.[Jia-Wei], Sigal, L.[Leonid], Mori, G.[Greg],
A Variational Auto-Encoder Model for Stochastic Point Processes,
CVPR19(3160-3169).
IEEE DOI 2002
model for action sequences. BibRef

Zhang, Y.[Yubo], Tokmakov, P.[Pavel], Hebert, M.[Martial], Schmid, C.[Cordelia],
A Structured Model for Action Detection,
CVPR19(9967-9976).
IEEE DOI 2002
BibRef

Zhang, D.[Da], Dai, X.Y.[Xi-Yang], Wang, Y.F.[Yuan-Fang],
Dynamic Temporal Pyramid Network: A Closer Look at Multi-scale Modeling for Activity Detection,
ACCV18(IV:712-728).
Springer DOI 1906
BibRef

Rosenfeld, A.[Amir], Ullman, S.[Shimon],
Action Classification via Concepts and Attributes,
ICPR18(1499-1505)
IEEE DOI 1812
Training, Feature extraction, Visualization, Semantics, Support vector machines, Task analysis, Detectors BibRef

Rueda, F.M., Fink, G.A.[Gernot A.],
Learning Attribute Representation for Human Activity Recognition,
ICPR18(523-528)
IEEE DOI 1812
Task analysis, Feature extraction, Foot, Activity recognition, Time series analysis BibRef

Li, L., Zhang, Z., Huang, Y., Wang, L.,
Deep Temporal Feature Encoding for Action Recognition,
ICPR18(1109-1114)
IEEE DOI 1812
Encoding, Feature extraction, Training, Task analysis, Aggregates, Silicon BibRef

Zhu, J., Zhu, Z., Zou, W.,
End-to-end Video-level Representation Learning for Action Recognition,
ICPR18(645-650)
IEEE DOI 1812
Streaming media, Training, Optical imaging, Adaptation models, Aggregates, Kinetic theory, Pattern recognition BibRef

Zhu, Y., Long, Y., Guan, Y., Newsam, S., Shao, L.,
Towards Universal Representation for Unseen Action Recognition,
CVPR18(9436-9445)
IEEE DOI 1812
Semantics, Visualization, Training, Pipelines, Kernel, Feature extraction, Encoding BibRef

Sironi, A., Brambilla, M., Bourdis, N., Lagorce, X., Benosman, R.,
HATS: Histograms of Averaged Time Surfaces for Robust Event-Based Object Classification,
CVPR18(1731-1740)
IEEE DOI 1812
Cameras, Feature extraction, Robot vision systems, Standards, Detectors BibRef

Panagiotakis, C.[Costas], Karvounas, G., Argyros, A.A.[Antonis A.],
Unsupervised Detection of Periodic Segments in Videos,
ICIP18(923-927)
IEEE DOI 1809
Videos, Motion segmentation, Time series analysis, YouTube, Animals, Visualization, periodicity detection, temporal video segmentation BibRef

Ahmad, K., Mekhalfi, M.L., Conci, N., Boato, G., Melgani, F., de Natale, F.G.B.,
A pool of deep models for event recognition,
ICIP17(2886-2890)
IEEE DOI 1803
Computational modeling, Feature extraction, Image recognition, Pipelines, Task analysis, score-level fusion BibRef

Doermann, D.,
IOD-CNN: Integrating object detection networks for event recognition,
ICIP17(875-879)
IEEE DOI 1803
Image recognition, Object detection, Search problems, Task analysis, Testing, Training, CNN architecture, object detection BibRef

Batabyal, T.,
DDT: Decentralized event Detection and Tracking using an ensemble of vertex-reinforced walks on a graph,
Southwest18(145-148)
IEEE DOI 1809
Topology, Event detection, Spatiotemporal phenomena, Time series analysis, Market research, Bipartite graph, graph structure BibRef

Batabyal, T., Sarkar, R., Acton, S.T.,
GraDED: A graph-based parametric dictionary learning algorithm for event detection,
ICIP17(1797-1801)
IEEE DOI 1803
Cameras, Coherence, Dictionaries, Event detection, Heuristic algorithms, Laplace equations, Machine learning, graph theory BibRef

Chen, S., Ma, B., Luo, P.,
Generalized pooling pyramid with hierarchical dictionary sparse coding for event and object recognition,
ICIP17(2349-2353)
IEEE DOI 1803
Dictionaries, Encoding, Image coding, Image recognition, Object recognition, Task analysis, Training BibRef

Qi, S., Huang, S., Wei, P., Zhu, S.C.,
Predicting Human Activities Using Stochastic Grammar,
ICCV17(1173-1181)
IEEE DOI 1802
grammars, graph theory, inference mechanisms, object detection, prediction theory, stochastic processes, video signal processing, Videos BibRef