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Real Time Motion Changes for New Event Detection and Recognition,
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Crispim-Junior, C.F.[Carlos Fernando], Buso, V., Avgerinakis, K., Meditskos, G., Briassouli, A., Benois-Pineau, J., Kompatsiaris, I.Y., Bremond, F.[Francois],
Semantic Event Fusion of Different Visual Modality Concepts for Activity Recognition,
PAMI(38), No. 8, August 2016, pp. 1598-1611.
IEEE DOI 1608
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Large scale Gaussian Process for overlap-based object proposal scoring,
CVIU(150), No. 1, 2016, pp. 95-108.
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Asymmetric kernel in Gaussian Processes for learning target variance,
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Elsevier DOI 1805
Gaussian process, Kernel metric learning, Asymmetric kernel distances, Regression BibRef

Pintea, S.L.[Silvia L.], van Gemert, J.C.[Jan C.], Smeulders, A.W.M.[Arnold W. M.],
Déjà Vu: Motion Prediction in Static Images,
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Springer DOI 1408
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Pintea, S.L.[Silvia L.], van Gemert, J.C.[Jan C.],
Making a Case for Learning Motion Representations with Phase,
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Springer DOI 1611
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Featureless: Bypassing feature extraction in action categorization,
ICIP16(196-200)
IEEE DOI 1610
Boosting BibRef

Yu, L., Yang, Y., Huang, Z., Wang, P., Song, J., Shen, H.T.,
Web Video Event Recognition by Semantic Analysis From Ubiquitous Documents,
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IEEE DOI 1612
Dataset, Video Events. UQE50 Dataset. UQ Event database with 50 pre-defined video events. Internet BibRef

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IP(26), No. 5, May 2017, pp. 2149-2162.
IEEE DOI 1704
Adaptation models BibRef

Gao, L.L.[Lian-Li], Li, T.[Tao], Song, J.K.[Jing-Kuan], Zhao, Z.[Zhou], Shen, H.T.[Heng Tao],
Play and rewind: Context-aware video temporal action proposals,
PR(107), 2020, pp. 107477.
Elsevier DOI 2008
Temporal action proposal generation and detection, Deep learning, Untrimmed video analysis BibRef

Li, C.[Chao], Cao, J.W.[Jie-Wei], Huang, Z.[Zi], Zhu, L., Shen, H.T.[Heng Tao],
Leveraging Weak Semantic Relevance for Complex Video Event Classification,
ICCV17(3667-3676)
IEEE DOI 1802
feature extraction, image classification, image motion analysis, learning (artificial intelligence), object detection, Visualization BibRef

Zhen, X.T.[Xian-Tong], Zheng, F.[Feng], Shao, L.[Ling], Cao, X.B.[Xian-Bin], Xu, D.[Dan],
Supervised Local Descriptor Learning for Human Action Recognition,
MultMed(19), No. 9, September 2017, pp. 2056-2065.
IEEE DOI 1708
Feature extraction, Image recognition, Kernel, Manifolds, Measurement, Supervised learning, Visualization, Action recognition, dimensionality reduction, image-to-class distance, large scale local features, manifold regularization, naive, Bayes, nearest, neighbor BibRef

Zheng, F.[Feng], Shao, L.[Ling], Song, Z.[Zhan],
Eigen-space learning using semi-supervised diffusion maps for human action recognition,
CIVR10(151-157).
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A set of co-occurrence matrices on the intrinsic manifold of human silhouettes for action recognition,
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Shao, L.[Ling], Ji, L.[Ling],
A descriptor combining MHI and PCOG for human motion classification,
CIVR10(236-242).
DOI Link 1007
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Motion Histogram Analysis Based Key Frame Extraction for Human Action/Activity Representation,
CRV09(88-92).
IEEE DOI 0905
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Shao, L.[Ling], Mattivi, R.[Riccardo],
Feature detector and descriptor evaluation in human action recognition,
CIVR10(477-484).
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Jiang, Y.G.[Yu-Gang], Wu, Z.X.[Zu-Xuan], Wang, J.[Jun], Xue, X.Y.[Xiang-Yang], Chang, S.F.[Shih-Fu],
Exploiting Feature and Class Relationships in Video Categorization with Regularized Deep Neural Networks,
PAMI(40), No. 2, February 2018, pp. 352-364.
IEEE DOI 1801
Existence of a particular human action or a complex event. Benchmark testing, Correlation, Feature extraction, Internet, Neural networks, Semantics, Visualization, Video categorization, regularization BibRef

Lai, K.T.[Kuan-Ting], Yu, F.X.[Felix X.], Chen, M.S.[Ming-Syan], Chang, S.F.[Shih-Fu],
Video Event Detection by Inferring Temporal Instance Labels,
CVPR14(2251-2258)
IEEE DOI 1409
Multiple Instance Learning; Proportion SVM; Video Event Detection BibRef

Lai, K.T.[Kuan-Ting], Liu, D.[Dong], Chen, M.S.[Ming-Syan], Chang, S.F.[Shih-Fu],
Recognizing Complex Events in Videos by Learning Key Static-Dynamic Evidences,
ECCV14(III: 675-688).
Springer DOI 1408
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Soleimani, H.[Hossein], Hensman, J.[James], Saria, S.[Suchi],
Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction,
PAMI(40), No. 8, August 2018, pp. 1948-1963.
IEEE DOI 1807
Computational modeling, Data models, Detectors, Predictive models, Reliability, Time series analysis, Uncertainty, time series BibRef

Tralie, C., Perea, J.,
(Quasi)Periodicity Quantification in Video Data, Using Topology,
SIIMS(11), No. 2, 2018, pp. 1049-1077.
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IEEE DOI 1906
Videos, Semantics, Visualization, Event detection, Task analysis, Encoding, Noise measurement, Segment-level, visual topics, event epitomes BibRef

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Event analysis, Regularization frameworks, Graph-based learning BibRef

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CirSysVideo(30), No. 11, November 2020, pp. 4232-4244.
IEEE DOI 2011
Proposals, Videos, Generators, Hybrid power systems, Moon, Reliability, SRG BibRef

Senocak, A.[Arda], Oh, T.H.[Tae-Hyun], Kim, J.[Junsik], Yang, M.H.[Ming-Hsuan], Kweon, I.S.[In So],
Learning to Localize Sound Sources in Visual Scenes: Analysis and Applications,
PAMI(43), No. 5, May 2021, pp. 1605-1619.
IEEE DOI 2104
BibRef
Earlier:
Learning to Localize Sound Source in Visual Scenes,
CVPR18(4358-4366)
IEEE DOI 1812
Where in scene did sound come from. Visualization, Videos, Task analysis, Correlation, Deep learning, Network architecture, Unsupervised learning, cross-modal retrieval. Position measurement. BibRef

Zhao, L.[Liang],
Event Prediction in the Big Data Era: A Systematic Survey,
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big data, Event prediction, artificial intelligence BibRef

Messikommer, N.[Nico], Gehrig, D.[Daniel], Gehrig, M.[Mathias], Scaramuzza, D.[Davide],
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IEEE DOI 2209
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Springer DOI 1810
Videos, Feature extraction, Training, Recurrent neural networks, Semantics, Principal component analysis, Deep learning, spatio-temporal pyramid BibRef

Li, L.[Li], Yuan, C.F.[Chun-Feng], Hu, W.M.[Wei-Ming], Li, B.[Bing],
Top-Down Cues for Event Recognition,
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Springer DOI 1011
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Li, L.[Li], Hu, W.M.[Wei-Ming], Li, B.[Bing], Yuan, C.F.[Chun-Feng], Zhu, P.F.[Peng-Fei], Li, W.Q.[Wan-Qing],
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IEEE DOI 1008
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Levinkov, E.[Evgeny], Kardoost, A.[Amirhossein], Andres, B.[Bjoern], Keuper, M.[Margret],
Higher-Order Multicuts for Geometric Model Fitting and Motion Segmentation,
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IEEE DOI 2212
Motion segmentation, Geometric modeling, Costs, Task analysis, Data models, Computational modeling, Combinatorial optimization, geometric model fitting BibRef

Keuper, M.[Margret],
Higher-Order Minimum Cost Lifted Multicuts for Motion Segmentation,
ICCV17(4252-4260)
IEEE DOI 1802
graph theory, image motion analysis, image segmentation, Higher-Order Minimum Cost Lifted Multicuts, Partitioning algorithms BibRef

Keuper, M., Andres, B.[Bjoern], Brox, T.,
Motion Trajectory Segmentation via Minimum Cost Multicuts,
ICCV15(3271-3279)
IEEE DOI 1602
Color BibRef

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Solving Minimum Cost Lifted Multicut Problems by Node Agglomeration,
ACCV18(IV:74-89).
Springer DOI 1906
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Bashmal, L.[Laila], Bazi, Y.[Yakoub], Al Rahhal, M.M.[Mohamad Mahmoud], Zuair, M.[Mansour], Melgani, F.[Farid],
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Elsevier DOI 2305
Temporal action proposal, Contexts, Attention model BibRef

Koutras, P., Zlatinsi, A., Maragos, P.,
Exploring CNN-Based Architectures for Multimodal Salient Event Detection in Videos,
IVMSP18(1-5)
IEEE DOI 1809
Videos, Visualization, Event detection, Motion pictures, Kernel BibRef

Cavaliere, D., Greco, L., Ritrovato, P., Senatore, S.,
A knowledge-based approach for video event detection using spatio-temporal sliding windows,
AVSS17(1-6)
IEEE DOI 1806
autonomous aerial vehicles, control engineering computing, knowledge based systems, navigation, object tracking, Video sequences BibRef

Pei, W.J.[Wen-Jie], Baltrušaitis, T.[Tadas], Tax, D.M.J.[David M.J.], Morency, L.P.[Louis-Philippe],
Temporal Attention-Gated Model for Robust Sequence Classification,
CVPR17(820-829)
IEEE DOI 1711
Computational modeling, Data models, Hidden Markov models. To deal with noisy sequence data. Apply to: spoken digit recognition, text-based sentiment analysis and visual event recognition. BibRef

Sobhani, F., Chandramouli, K., Zhang, Q.N.[Qian-Ni], Izquierdo, E.[Ebroul],
Formal representation of events in a surveillance domain ontology,
ICIP16(913-917)
IEEE DOI 1610
Cameras BibRef

Liu, X.L.[Xue-Liang], Wang, F.F.[Fei-Fei], Huet, B.[Benoit], Wang, F.[Feng],
E2SGM: Event Enrichment and Summarization by Graph Model,
MMMod16(II: 348-353).
Springer DOI 1601
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Tzelepis, C., Mavridaki, E., Mezaris, V., Patras, I.,
Video aesthetic quality assessment using kernel Support Vector Machine with isotropic Gaussian sample uncertainty (KSVM-IGSU),
ICIP16(2410-2414)
IEEE DOI 1610
Cameras BibRef

Tzelepis, C.[Christos], Mezaris, V.[Vasileios], Patras, I.[Ioannis],
Video Event Detection Using Kernel Support Vector Machine with Isotropic Gaussian Sample Uncertainty (KSVM-iGSU),
MMMod16(I: 3-15).
Springer DOI 1601
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Zografos, V.[Vasileios], Lenz, R.[Reiner], Ringaby, E.[Erik], Felsberg, M.[Michael], Nordberg, K.[Klas],
Fast Segmentation of Sparse 3D Point Trajectories Using Group Theoretical Invariants,
ACCV14(IV: 675-691).
Springer DOI 1504
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Trichet, R.[Remi], Nevatia, R.[Ramakant],
Video Segmentation Descriptors for Event Recognition,
ICPR14(1940-1945)
IEEE DOI 1412
Color BibRef

Chen, S.Z.[Shi-Zhe], Huang, D.[Dong],
Elaborative Rehearsal for Zero-shot Action Recognition,
ICCV21(13618-13627)
IEEE DOI 2203
Training, Protocols, Codes, Target recognition, Semantics, Benchmark testing, Action and behavior recognition, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Huang, D.[Dong], Yao, S.T.[Shi-Tong], Wang, Y.[Yi], de la Torre, F.[Fernando],
Sequential Max-Margin Event Detectors,
ECCV14(III: 410-424).
Springer DOI 1408
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Kumar, K.P., Parameswaran, L.,
A hybrid method for object identification and event detection in video,
NCVPRIPG13(1-4)
IEEE DOI 1408
image colour analysis BibRef

Bossard, L.[Lukas], Guillaumin, M.[Matthieu], Van Gool, L.J.[Luc J.],
Event Recognition in Photo Collections with a Stopwatch HMM,
ICCV13(1193-1200)
IEEE DOI 1403
Dataset, Event Recognition. 61,000 images in 807 collections, with 14 social event classes. BibRef

Jain, A.[Arpit], Gupta, A.[Abhinav], Rodriguez, M.[Mikel], Davis, L.S.[Larry S.],
Representing Videos Using Mid-level Discriminative Patches,
CVPR13(2571-2578)
IEEE DOI 1309
Action Recognition; Video Understanding. Patches match across videos. Alignment, Action. BibRef

Irvine, J., Young, M.[Mon], Deutsch, O., Antelman, E., Guler, S., Morde, A., Ma, X.[Xiang], Pushee, I.,
Enhanced event recognition in video using image quality assessment,
AIPR12(1-8)
IEEE DOI 1307
computer vision BibRef

Izadinia, H.[Hamid], Shah, M.[Mubarak],
Recognizing Complex Events Using Large Margin Joint Low-Level Event Model,
ECCV12(IV: 430-444).
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Miller, B.[Ben], McCloskey, S.[Scott],
Metric Feature Indexing for Interactive Multimedia Search,
CRV16(109-115)
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activity recognition;indexing;machine learning BibRef

McCloskey, S.[Scott], Davalos, P.[Pedro],
Activity detection in the wild using video metadata,
ICPR12(3140-3143).
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McCloskey, S.[Scott], Liu, J.C.[Jing-Chen],
Metadata-Weighted Score Fusion for Multimedia Event Detection,
CRV14(299-305)
IEEE DOI 1406
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Liu, J.C.[Jing-Chen], McCloskey, S.[Scott], Liu, Y.X.[Yan-Xi],
Local Expert Forest of Score Fusion for Video Event Classification,
ECCV12(V: 397-410).
Springer DOI 1210
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Castellanos, R.[Ricardo], Kalva, H.[Hari], Marques, O.[Oge], Furht, B.[Borko],
Event detection in video using motion analysis,
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Springer DOI 1109
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Burlet, J., Aycard, O., Baig, Q.,
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Robbel, P., Roy, D.,
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ICARCV10(2102-2108).
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A Hierarchical Representation for Future Action Prediction,
ECCV14(III: 689-704).
Springer DOI 1408
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Liu, J.G.[Jin-Gen], Kuipers, B.[Benjamin], Savarese, S.[Silvio],
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IEEE DOI 1106
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Vyas, S.[Shruti], Rawat, Y.S.[Yogesh S.], Shah, M.[Mubarak],
Multi-view Action Recognition Using Cross-view Video Prediction,
ECCV20(XXVII:427-444).
Springer DOI 2011
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Liu, J.G.[Jin-Gen], Shah, M.[Mubarak], Kuipers, B.[Benjamin], Savarese, S.[Silvio],
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Park, D.J.[Dong-Jun], Eichmann, D.[David],
Video Event Detection as Matching of Spatiotemporal Projection,
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Springer DOI 1011
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Yildirim, Y.[Yakup], Yilmaz, T.[Turgay], Yazici, A.[Adnan],
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IEEE DOI 1009
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IEEE DOI 0909
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DICTA09(188-195).
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Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Trajectory Analysis for Events, Actions .