16.7.4.4.1 Pedestrian Trajectory Analysis, Pedestrian Tracking

Piotto, N.[Nicola], Conci, N.[Nicola], de Natale, F.G.B.[Francesco G.B.],
Syntactic Matching of Trajectories for Ambient Intelligence Applications,
MultMed(11), No. 7, November 2009, pp. 1266-1275.
IEEE DOI 0911
BibRef
And: A1, A3, A2:
Hierarchical Matching of 3D Pedestrian Trajectories for Surveillance Applications,
AVSBS09(146-151).
IEEE DOI 0909
BibRef

Pellegrini, S.[Stefano], Van Gool, L.J.[Luc J.],
Tracking with a mixed continuous-discrete Conditional Random Field,
CVIU(117), No. 10, 2013, pp. 1215-1228.
Elsevier DOI 1309
Tracking BibRef

Pellegrini, S.[Stefano], Ess, A.[Andreas], Van Gool, L.J.[Luc J.],
Improving Data Association by Joint Modeling of Pedestrian Trajectories and Groupings,
ECCV10(I: 452-465).
Springer DOI 1009
BibRef

Pellegrini, S., Ess, A.[Andreas], Schindler, K.[Konrad], Van Gool, L.J.,
You'll never walk alone: Modeling social behavior for multi-target tracking,
ICCV09(261-268).
IEEE DOI 0909
BibRef

Ess, A.[Andreas], Leibe, B.[Bastian], Van Gool, L.J.[Luc J.],
Depth and Appearance for Mobile Scene Analysis,
ICCV07(1-8).
IEEE DOI 0710
BibRef

Liu, W., Lau, R.W.H., Wang, X.G.[Xiao-Gang], Manocha, D.[Dinesh],
Exemplar-AMMs: Recognizing Crowd Movements From Pedestrian Trajectories,
MultMed(18), No. 12, December 2016, pp. 2398-2406.
IEEE DOI 1612
Computational modeling BibRef

Antonini, G.[Gianluca], Martinez, S.V.[Santiago Venegas], Bierlaire, M.[Michel], Thiran, J.P.[Jean Philippe],
Behavioral Priors for Detection and Tracking of Pedestrians in Video Sequences,
IJCV(69), No. 2, August 2006, pp. 159-180.
Springer DOI 0606
BibRef
Earlier: A2, A1, A4, A3:
Bayesian integration of a discrete choice pedestrian behavioral model and image correlation techniques for automatic multi object tracking,
ICIP04(II: 1037-1040).
IEEE DOI 0505
BibRef

Alahi, A.[Alexandre], Marimon, D.[David], Bierlaire, M.[Michel], Kunt, M.[Murat],
A master-slave approach for object detection and matching with fixed and mobile cameras,
ICIP08(1712-1715).
IEEE DOI 0810
BibRef
Earlier: A1, A3, A4, Only:
Object Detection and Matching with Mobile Cameras Collaborating with Fixed Cameras,
M2SFA208(xx-yy). 0810
Primarily for pedestrians. BibRef

Alahi, A.[Alexandre], Vandergheynst, P.[Pierre], Bierlaire, M.[Michel], Kunt, M.[Murat],
Cascade of descriptors to detect and track objects across any network of cameras,
CVIU(114), No. 6, June 2010, pp. 624-640.
Elsevier DOI 1006
Object detection; Object tracking; Region descriptors; Cascade of descriptors; Multi-view; Mobile cameras; Pedestrian recognition BibRef

Biliotti, D.[David], Antonini, G.[Gianluca], Thiran, J.P.[Jean Philippe],
Multi-Layer Hierarchical Clustering of Pedestrian Trajectories for Automatic Counting of People in Video Sequences,
Motion05(II: 50-57).
IEEE DOI 0502
BibRef

Antonini, G.[Gianluca], Thiran, J.P.[Jean Philippe],
Counting Pedestrians in Video Sequences Using Trajectory Clustering,
CirSysVideo(16), No. 8, August 2006, pp. 1008-1020.
IEEE DOI 0609
BibRef

Yang, L.[Lie], Hu, G.H.[Guang-Hua], Song, Y.H.[Yong-Hao], Li, G.F.[Guo-Feng], Xie, L.H.[Long-Han],
Intelligent video analysis: A Pedestrian trajectory extraction method for the whole indoor space without blind areas,
CVIU(196), 2020, pp. 102968.
Elsevier DOI 2006
Fisheye camera, Pedestrian detection, Object tracking, Height estimation, Trajectory extraction BibRef

Zhou, C.J.[Cheng-Ju], Wu, M.Q.[Mei-Qing], Lam, S.K.[Siew-Kei],
Group Cost-Sensitive BoostLR With Vector Form Decorrelated Filters for Pedestrian Detection,
ITS(21), No. 12, December 2020, pp. 5022-5035.
IEEE DOI 2012
Feature extraction, Decorrelation, Training, Computational complexity, Testing, Boosting, Pedestrian detection, BibRef

Haddad, S.[Sirin], Lam, S.K.[Siew-Kei],
Self-Growing Spatial Graph Network for Context-Aware Pedestrian Trajectory Prediction,
ICIP21(1029-1033)
IEEE DOI 2201
BibRef
Earlier:
Self-Growing Spatial Graph Networks for Pedestrian Trajectory Prediction,
WACV20(1140-1148)
IEEE DOI 2006
Adaptation models, Visualization, Adaptive systems, Machine learning, Predictive models, Spatial databases, Trajectory, Nonnegative Matrix Factorization. Trajectory, Predictive models, Task analysis, Dynamics, Data models BibRef

Sawas, A.[Abdullah], Abuolaim, A.[Abdullah], Afifi, M.[Mahmoud], Papagelis, M.[Manos],
A versatile computational framework for group pattern mining of pedestrian trajectories,
GeoInfo(23), No. 4, October 2019, pp. 501-531.
WWW Link. 1911
BibRef

Chen, K.[Kai], Song, X.[Xiao], Ren, X.X.[Xiao-Xiang],
Pedestrian Trajectory Prediction in Heterogeneous Traffic Using Pose Keypoints-Based Convolutional Encoder-Decoder Network,
CirSysVideo(31), No. 5, 2021, pp. 1764-1775.
IEEE DOI 2105
BibRef

Wang, R.P.[Rui-Ping], Cui, Y.[Yong], Song, X.[Xiao], Chen, K.[Kai], Fang, H.[Hong],
Multi-information-based convolutional neural network with attention mechanism for pedestrian trajectory prediction,
IVC(107), 2021, pp. 104110.
Elsevier DOI 2103
Depth map, Pose, 2D-3D size information, Convolutional neural network, Trajectory prediction BibRef

Chen, K.[Kai], Song, X.[Xiao], Yuan, H.T.[Hai-Tao], Ren, X.X.[Xiao-Xiang],
Fully Convolutional Encoder-Decoder With an Attention Mechanism for Practical Pedestrian Trajectory Prediction,
ITS(23), No. 11, November 2022, pp. 20046-20060.
IEEE DOI 2212
Trajectory, Predictive models, Feature extraction, Convolutional neural networks, Markov processes, Force, attention mechanism BibRef

Song, X.[Xiao], Chen, K.[Kai], Li, X.[Xu], Sun, J.H.[Jing-Han], Hou, B.C.[Bao-Cun], Cui, Y.[Yong], Zhang, B.C.[Bao-Chang], Xiong, G.[Gang], Wang, Z.L.[Zi-Lie],
Pedestrian Trajectory Prediction Based on Deep Convolutional LSTM Network,
ITS(22), No. 6, June 2021, pp. 3285-3302.
IEEE DOI 2106
Trajectory, Predictive models, Neural networks, Force, Mathematical model, Feature extraction, Tensors, neural network BibRef

Zhang, P.[Pu], Xue, J.R.[Jian-Ru], Zhang, P.F.[Peng-Fei], Zheng, N.N.[Nan-Ning], Ouyang, W.L.[Wan-Li],
Social-Aware Pedestrian Trajectory Prediction via States Refinement LSTM,
PAMI(44), No. 5, May 2022, pp. 2742-2759.
IEEE DOI 2204
BibRef
Earlier: A1, A5, A3, A2, A4:
SR-LSTM: State Refinement for LSTM Towards Pedestrian Trajectory Prediction,
CVPR19(12077-12086).
IEEE DOI 2002
Trajectory, Feature extraction, Legged locomotion, Predictive models, Neurons, Message passing, Adaptation models, message passing BibRef

Quan, R., Zhu, L., Wu, Y., Yang, Y.,
Holistic LSTM for Pedestrian Trajectory Prediction,
IP(30), 2021, pp. 3229-3239.
IEEE DOI 2103
Trajectory, Vehicle dynamics, Logic gates, Dynamics, Roads, Correlation, Task analysis, Pedestrian trajectory prediction, pedestrian intention BibRef

Zhou, Y.[Yutao], Wu, H.Y.[Hua-Yi], Cheng, H.Q.[Hong-Quan], Qi, K.L.[Kun-Lun], Hu, K.[Kai], Kang, C.G.[Chao-Gui], Zheng, J.[Jie],
Social graph convolutional LSTM for pedestrian trajectory prediction,
IET-ITS(15), No. 3, 2021, pp. 396-405.
DOI Link 2106
BibRef

Zamboni, S.[Simone], Kefato, Z.T.[Zekarias Tilahun], Girdzijauskas, S.[Sarunas], Norén, C.[Christoffer], Col, L.D.[Laura Dal],
Pedestrian trajectory prediction with convolutional neural networks,
PR(121), 2022, pp. 108252.
Elsevier DOI 2109
Trajectory prediction, Pedestrian prediction, Convolutional neural networks BibRef

Yao, H.Y.[Hai-Yan], Wan, W.G.[Wang-Gen], Li, X.[Xiang],
End-to-End Pedestrian Trajectory Forecasting with Transformer Network,
IJGI(11), No. 1, 2022, pp. xx-yy.
DOI Link 2201
BibRef

Kong, W.[Wei], Liu, Y.[Yun], Li, H.[Hui], Wang, C.X.[Chuan-Xu], Tao, Y.[Ye], Kong, X.Z.[Xiang-Zhen],
GSTA: Pedestrian trajectory prediction based on global spatio-temporal association of graph attention network,
PRL(160), 2022, pp. 90-97.
Elsevier DOI 2208
Pedestrian trajectory, Trajectory prediction, Receptive field, Attention mechanism, Spatio-temporal garph, Graph convolution BibRef

Li, L.H.[Lin-Hui], Zhou, B.[Bin], Lian, J.[Jing], Wang, X.C.[Xue-Cheng], Zhou, Y.F.[Ya-Fu],
Multi-PPTP: Multiple Probabilistic Pedestrian Trajectory Prediction in the Complex Junction Scene,
ITS(23), No. 8, August 2022, pp. 13758-13768.
IEEE DOI 2208
Trajectory, Predictive models, Junctions, Feature extraction, Semantics, Real-time systems, Automobiles, Autonomous driving, trajectory prediction BibRef

Pang, S.M.[Shu Min], Cao, J.X.[Jin Xin], Jian, M.Y.[Mei Ying], Lai, J.[Jian], Yan, Z.Y.[Zhen Ying],
BR-GAN: A Pedestrian Trajectory Prediction Model Combined With Behavior Recognition,
ITS(23), No. 12, December 2022, pp. 24609-24620.
IEEE DOI 2212
Trajectory, Behavioral sciences, Predictive models, Semantics, Generative adversarial networks, Legged locomotion, Software, trajectory prediction BibRef

Zhou, H.[Hao], Ren, D.C.[Dong-Chun], Yang, X.[Xu], Fan, M.Y.[Ming-Yu], Huang, H.[Hai],
CSR: Cascade Conditional Variational Auto Encoder with Socially-aware Regression for Pedestrian Trajectory Prediction,
PR(133), 2023, pp. 109030.
Elsevier DOI 2210
Pedestrian trajectory prediction, Socially-aware model, Conditional variational autoencoder (CVAE) BibRef

Wang, D.[Dafeng], Liu, H.B.[Hong-Bo], Wang, N.[Naiyao], Wang, Y.[Yiyang], Wang, H.[Hua], McLoone, S.[Seán],
SEEM: A Sequence Entropy Energy-Based Model for Pedestrian Trajectory All-Then-One Prediction,
PAMI(45), No. 1, January 2023, pp. 1070-1086.
IEEE DOI 2212
Trajectory, Predictive models, Generators, Entropy, Stability analysis, Potential energy, Training, potential energy regularization BibRef

Lian, J.[Jing], Yu, F.N.[Feng-Ning], Li, L.H.[Lin-Hui], Zhou, Y.[Yafu],
Causal Temporal-Spatial Pedestrian Trajectory Prediction With Goal Point Estimation and Contextual Interaction,
ITS(23), No. 12, December 2022, pp. 24499-24509.
IEEE DOI 2212
Trajectory, Predictive models, Feature extraction, Transformers, Task analysis, Semantics, Decoding, transformer BibRef

Korbmacher, R.[Raphael], Tordeux, A.[Antoine],
Review of Pedestrian Trajectory Prediction Methods: Comparing Deep Learning and Knowledge-Based Approaches,
ITS(23), No. 12, December 2022, pp. 24126-24144.
IEEE DOI 2212
Trajectory, Predictive models, Force, Microscopy, Mathematical models, Knowledge based systems, Dynamics, knowledge-based models BibRef

Kothari, P.[Parth], Alahi, A.[Alexandre],
Safety-Compliant Generative Adversarial Networks for Human Trajectory Forecasting,
ITS(24), No. 4, April 2023, pp. 4251-4261.
IEEE DOI 2304
Trajectory, Predictive models, Generators, Forecasting, Transformers, Biological system modeling, Generative adversarial networks, multimodality BibRef

Huynh, M.[Manh], Alaghband, G.[Gita],
Online Adaptive Temporal Memory with Certainty Estimation for Human Trajectory Prediction,
WACV23(940-949)
IEEE DOI 2302
Adaptation models, Navigation, Computational modeling, Dynamics, Estimation, Predictive models, Robotics BibRef

Chen, J.Y.[Jiu-Yu], Wang, Z.L.[Zhong-Li], Wang, J.[Jian],
OA-STGCN: An Output Anchoring-based Graph Convolutional Network for Human Trajectory Prediction,
ICRVC22(320-324)
IEEE DOI 2301
Measurement, Convolution, Decision making, Psychology, Prediction methods, Prediction algorithms, Trajectory, trajectory prediction BibRef

Li, L.[Lihuan], Pagnucco, M.[Maurice], Song, Y.[Yang],
Graph-based Spatial Transformer with Memory Replay for Multi-future Pedestrian Trajectory Prediction,
CVPR22(2221-2231)
IEEE DOI 2210
Robot motion, Analytical models, Smoothing methods, Computational modeling, Predictive models, Transformers, Video analysis and understanding BibRef

Song, Y.[Yue], Bisagno, N.[Niccoló], Hassan, S.Z.[Syed Zohaib], Conci, N.[Nicola],
AG-GAN: An Attentive Group-Aware GAN for pedestrian trajectory prediction,
ICPR21(8703-8710)
IEEE DOI 2105
Predictive models, Benchmark testing, Generative adversarial networks, Trajectory, Pattern recognition, History BibRef

Shi, L.S.[Liu-Shuai], Wang, L.[Le], Long, C.J.[Cheng-Jiang], Zhou, S.P.[San-Ping], Zhou, M.[Mo], Niu, Z.X.[Zhen-Xing], Hua, G.[Gang],
SGCN: Sparse Graph Convolution Network for Pedestrian Trajectory Prediction,
CVPR21(8990-8999)
IEEE DOI 2111
Legged locomotion, Adaptation models, Visualization, Convolution, Predictive models, Trajectory BibRef

Dendorfer, P.[Patrick], Elflein, S.[Sven], Leal-Taixé, L.[Laura],
MG-GAN: A Multi-Generator Model Preventing Out-of-Distribution Samples in Pedestrian Trajectory Prediction,
ICCV21(13138-13147)
IEEE DOI 2203
Measurement, Predictive models, Generative adversarial networks, Routing, Generators, BibRef

Bi, H.K.[Hui-Kun], Zhang, R.[Ruisi], Mao, T.[Tianlu], Deng, Z.G.[Zhi-Gang], Wang, Z.Q.[Zhao-Qi],
How Can I See My Future? FvTraj: Using First-person View for Pedestrian Trajectory Prediction,
ECCV20(VII:576-593).
Springer DOI 2011
BibRef

Yu, C.J.[Cun-Jun], Ma, X.[Xiao], Ren, J.W.[Jia-Wei], Zhao, H.[Haiyu], Yi, S.[Shuai],
Spatio-temporal Graph Transformer Networks for Pedestrian Trajectory Prediction,
ECCV20(XII: 507-523).
Springer DOI 2010
BibRef

Styles, O., Guha, T., Sanchez, V., Kot, A.C.,
Multi-Camera Trajectory Forecasting: Pedestrian Trajectory Prediction in a Network of Cameras,
Precognition20(4379-4382)
IEEE DOI 2008
Cameras, Trajectory, Task analysis, Forecasting, Databases, Computational modeling, Object detection BibRef

Habibi, G., Jaipuria, N., How, J.P.,
SILA: An Incremental Learning Approach for Pedestrian Trajectory Prediction,
Precognition20(4411-4421)
IEEE DOI 2008
Trajectory, Hidden Markov models, Training, Prediction algorithms, Data models, Predictive models, Encoding BibRef

Xue, H.[Hao], Huynh, D.[Du], Reynolds, M.[Mark],
Location-Velocity Attention for Pedestrian Trajectory Prediction,
WACV19(2038-2047)
IEEE DOI 1904
BibRef
Earlier:
SS-LSTM: A Hierarchical LSTM Model for Pedestrian Trajectory Prediction,
WACV18(1186-1194)
IEEE DOI 1806
BibRef
And:
Bi-Prediction: Pedestrian Trajectory Prediction Based on Bidirectional LSTM Classification,
DICTA17(1-8)
IEEE DOI 1804
learning (artificial intelligence), pedestrians, recurrent neural nets, pedestrian trajectory prediction, Task analysis. feature extraction, image recognition, learning (artificial intelligence), neural nets, Trajectory. image classification, object detection. BibRef

Hasan, I., Setti, F., Tsesmelis, T., del Bue, A., Cristani, M., Galasso, F.,
'Seeing is Believing': Pedestrian Trajectory Forecasting Using Visual Frustum of Attention,
WACV18(1178-1185)
IEEE DOI 1806
image motion analysis, minimisation, pedestrians, pose estimation, collision avoidance, destination point, expected destination, Visualization BibRef

Maki, A.[Atsuto], Seki, A.[Akihito], Watanabe, T.[Tomoki], Cipolla, R.[Roberto],
Co-occurrence flow for pedestrian detection,
ICIP11(1889-1892).
IEEE DOI 1201
BibRef

Galasso, F.[Fabio], Iwasaki, M.[Masahiro], Nobori, K.[Kunio], Cipolla, R.[Roberto],
Spatio-temporal clustering of probabilistic region trajectories,
ICCV11(1738-1745).
IEEE DOI 1201
for pedestrian trajectories BibRef

Ricci, E.[Elisa], Tobia, F.[Francesco], Zen, G.[Gloria],
Learning Pedestrian Trajectories with Kernels,
ICPR10(149-152).
IEEE DOI 1008
BibRef

Nishio, S.[Shuichi], Okamoto, H.[Hiromi], Babaguchi, N.[Noboru],
Hierarchical Anomality Detection Based on Situation,
ICPR10(1108-1111).
IEEE DOI 1008
Pedestrian trajectories. BibRef

Ellis, D.[David], Sommerlade, E.[Eric], Reid, I.D.[Ian D.],
Modelling pedestrian trajectory patterns with Gaussian processes,
VS09(1229-1234).
IEEE DOI 0910

See also Action recognition using shared motion parts. BibRef

Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Crowds, Tracking Multiple People, Multiple Pedestrian Tracking .