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.F.[Da-Feng],
Liu, H.B.[Hong-Bo],
Wang, N.[Naiyao],
Wang, Y.Y.[Yi-Yang],
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
Chen, W.H.[Wei-Huang],
Yang, Z.G.[Zhi-Gang],
Xue, L.Y.[Ling-Yang],
Duan, J.H.[Jing-Hai],
Sun, H.B.[Hong-Bin],
Zheng, N.N.[Nan-Ning],
Multimodal Pedestrian Trajectory Prediction Using Probabilistic
Proposal Network,
CirSysVideo(33), No. 6, June 2023, pp. 2877-2891.
IEEE DOI
2306
Trajectory, Proposals, Probabilistic logic, Transformers, Generators,
Task analysis, Predictive models, proposal
BibRef
Zhu, W.J.[Wen-Jun],
Liu, Y.H.[Yang-Hong],
Zhang, M.Y.[Meng-Yi],
Yi, Y.[Yang],
Reciprocal Consistency Prediction Network for Multi-Step Human
Trajectory Prediction,
ITS(24), No. 6, June 2023, pp. 6042-6052.
IEEE DOI
2306
Trajectory, Predictive models, Reliability, Forecasting, Bidirectional control,
Training, Target tracking, Neural network, predictive confidence
BibRef
Chen, H.[Hao],
Liu, Y.[Yinhua],
Hu, C.[Chuan],
Zhang, X.[Xi],
Vulnerable Road User Trajectory Prediction for Autonomous Driving
Using a Data-Driven Integrated Approach,
ITS(24), No. 7, July 2023, pp. 7306-7317.
IEEE DOI
2307
Trajectory, Predictive models, Roads, Long short term memory,
Vehicle dynamics, Safety, Behavioral sciences, Autonomous driving,
VRU intention
BibRef
Xie, C.[Ce],
Li, Y.M.[Yuan-Man],
Liang, R.Q.[Rong-Qin],
Dong, L.[Li],
Li, X.[Xia],
Synchronous Bi-directional Pedestrian Trajectory Prediction with Error
Compensation,
ACCV22(VI:699-715).
Springer DOI
2307
BibRef
Zhong, X.[Xian],
Yan, X.[Xu],
Yang, Z.W.[Zheng-Wei],
Huang, W.X.[Wen-Xin],
Jiang, K.[Kui],
Liu, R.W.[Ryan Wen],
Wang, Z.[Zheng],
Visual Exposes You: Pedestrian Trajectory Prediction Meets Visual
Intention,
ITS(24), No. 9, September 2023, pp. 9390-9400.
IEEE DOI
2310
BibRef
Zhu, W.J.[Wen-Jun],
Liu, Y.H.[Yang-Hong],
Wang, P.[Peng],
Zhang, M.[Mengyi],
Wang, T.[Tian],
Yi, Y.[Yang],
Tri-HGNN: Learning triple policies fused hierarchical graph neural
networks for pedestrian trajectory prediction,
PR(143), 2023, pp. 109772.
Elsevier DOI
2310
Trajectory prediction, Hierarchical policy,
Graph neural networks,
BibRef
Golchoubian, M.[Mahsa],
Ghafurian, M.[Moojan],
Dautenhahn, K.[Kerstin],
Azad, N.L.[Nasser Lashgarian],
Pedestrian Trajectory Prediction in Pedestrian-Vehicle Mixed
Environments: A Systematic Review,
ITS(24), No. 11, November 2023, pp. 11544-11567.
IEEE DOI
2311
BibRef
Cheng, H.[Hao],
Liu, M.M.[Meng-Meng],
Chen, L.[Lin],
Broszio, H.[Hellward],
Sester, M.[Monika],
Yang, M.Y.[Michael Ying],
GATraj: A graph- and attention-based multi-agent trajectory
prediction model,
PandRS(205), 2023, pp. 163-175.
Elsevier DOI
2311
Trajectory prediction, Graph model, Autonomous driving,
Pedestrian, Mixture density network
BibRef
Liu, F.[Fugang],
Duan, S.[Songnan],
Juan, W.[Wang],
A pedestrian trajectory prediction method based on improved LSTM
network,
IET-IPR(18), No. 2, 2024, pp. 379-387.
DOI Link
2402
image processing
BibRef
Zhou, J.[Jincao],
Bai, X.[Xin],
Fu, W.P.[Wei-Ping],
Ning, B.[Benyu],
Li, R.[Rui],
Pedestrian intention estimation and trajectory prediction based on
data and knowledge-driven method,
IET-ITS(18), No. 2, 2024, pp. 315-331.
DOI Link
2402
autonomous driving, Bayes methods, intelligent transportation systems,
pedestrians, road safety
BibRef
Liu, Q.K.[Quan-Kai],
Sang, H.F.[Hai-Feng],
Wang, J.[Jinyu],
Chen, W.X.[Wang-Xing],
Liu, Y.L.[Yu-Long],
Non-probability sampling network based on anomaly pedestrian
trajectory discrimination for pedestrian trajectory prediction,
IVC(143), 2024, pp. 104954.
Elsevier DOI
2403
Pedestrian trajectory prediction, Non-probability sampling network,
Subtraction fusion network, First-person view
BibRef
Peng, Y.S.[Yu-Sheng],
Zhang, G.F.[Gao-Feng],
Shi, J.[Jun],
Li, X.Y.[Xiang-Yu],
Zheng, L.P.[Li-Ping],
MRGTraj:
A Novel Non-Autoregressive Approach for Human Trajectory Prediction,
CirSysVideo(34), No. 4, April 2024, pp. 2318-2331.
IEEE DOI Code:
WWW Link.
2404
Decoding, Trajectory, Predictive models, Pedestrians, Codes,
Gaussian distribution, Feature extraction, Trajectory prediction,
social interaction
BibRef
Xie, J.J.[Jia-Jia],
Zhang, S.[Sheng],
Xia, B.[Beihao],
Xiao, Z.[Zhu],
Jiang, H.B.[Hong-Bo],
Zhou, S.[Siwang],
Qin, Z.[Zheng],
Chen, H.Y.[Hong-Yang],
Pedestrian Trajectory Prediction Based on Social Interactions
Learning With Random Weights,
MultMed(26), 2024, pp. 7503-7515.
IEEE DOI
2405
Pedestrians, Trajectory, Generative adversarial networks, Training,
Long short term memory, Task analysis, Predictive models,
graph with random weights
BibRef
Wong, C.[Conghao],
Xia, B.[Beihao],
Zou, Z.Q.[Zi-Qian],
Wang, Y.L.[Yu-Long],
You, X.G.[Xin-Ge],
SocialCircle: Learning the Angle-based Social Interaction
Representation for Pedestrian Trajectory Prediction,
CVPR24(19005-19015)
IEEE DOI
2410
Training, Pedestrians, Uncertainty, Toy manufacturing industry,
Predictive models, Mathematical models
BibRef
Pei, Z.[Zhao],
Zhang, J.Q.[Jia-Qing],
Zhang, W.W.[Wen-Wen],
Wang, M.[Miao],
Wang, J.N.[Jia-Ning],
Yang, Y.H.[Yee-Hong],
Autofocusing for Synthetic Aperture Imaging Based on Pedestrian
Trajectory Prediction,
CirSysVideo(34), No. 5, May 2024, pp. 3551-3562.
IEEE DOI
2405
Pedestrians, Trajectory, Cameras, Apertures, Task analysis,
Predictive models, Vehicle dynamics, Light field, occlusion,
human social behavior simulation
BibRef
Chen, X.B.[Xiao-Bo],
Zhang, H.[Huanjia],
Deng, F.[Fuwen],
Liang, J.[Jun],
Yang, J.[Jian],
Stochastic Non-Autoregressive Transformer-Based Multi-Modal
Pedestrian Trajectory Prediction for Intelligent Vehicles,
ITS(25), No. 5, May 2024, pp. 3561-3574.
IEEE DOI Code:
WWW Link.
2405
Trajectory, Pedestrians, Predictive models, Transformers,
Feature extraction, Intelligent vehicles,
multi-modal prediction
BibRef
Yang, J.[Jing],
Chen, Y.[Yuehai],
Du, S.[Shaoyi],
Chen, B.D.[Ba-Dong],
Principe, J.C.[Jose C.],
IA-LSTM: Interaction-Aware LSTM for Pedestrian Trajectory Prediction,
Cyber(54), No. 7, July 2024, pp. 3904-3917.
IEEE DOI
2407
Pedestrians, Trajectory, Predictive models, Feature extraction,
Vehicle dynamics, Task analysis, Dynamics, Correntropy,
pedestrian trajectory prediction
BibRef
Zhang, Z.M.[Zheng-Ming],
Ding, Z.M.[Zheng-Ming],
Tian, R.[Renran],
Decouple Ego-View Motions for Predicting Pedestrian Trajectory and
Intention,
IP(33), 2024, pp. 4716-4727.
IEEE DOI
2409
Pedestrians, Trajectory, Predictive models, Autonomous vehicles,
Roads, Cameras, Automobiles, Pedestrian trajectory prediction,
pedestrian intention
BibRef
Nayak, A.[Anshul],
Eskandarian, A.[Azim],
Doerzaph, Z.[Zachary],
Ghorai, P.[Prasenjit],
Pedestrian Trajectory Forecasting Using Deep Ensembles Under Sensing
Uncertainty,
ITS(25), No. 9, September 2024, pp. 11317-11329.
IEEE DOI
2409
Trajectory, Uncertainty, Predictive models, Probabilistic logic,
Artificial neural networks, Sensors, Pedestrians, MC dropout
BibRef
Han, X.[Xiao],
Zhang, X.F.[Xin-Feng],
Wu, Y.L.[Yi-Ling],
Zhang, Z.D.[Zhen-Duo],
Zhang, T.Y.[Tian-Yu],
Wang, Y.W.[Yao-Wei],
Knowledge-Based Multiple Relations Modeling for Traffic Forecasting,
ITS(25), No. 9, September 2024, pp. 11844-11857.
IEEE DOI Code:
WWW Link.
2409
Forecasting, Knowledge graphs, Correlation, Predictive models, Tail,
Spatiotemporal phenomena, Knowledge based systems,
self-attention
BibRef
Kang, M.[Miao],
Wang, S.Q.[Sheng-Qi],
Zhou, S.P.[San-Ping],
Ye, K.[Ke],
Jiang, J.J.[Jing-Jing],
Zheng, N.N.[Nan-Ning],
FFINet: Future Feedback Interaction Network for Motion Forecasting,
ITS(25), No. 9, September 2024, pp. 12285-12296.
IEEE DOI
2409
Trajectory, Forecasting, Vectors, Predictive models,
Feedforward systems, Feature extraction, Vehicles,
cross-temporal aggregation
BibRef
Wu, Q.[Qi],
Zhou, S.P.[San-Ping],
Wang, L.[Le],
Shi, L.[Liushuai],
Dong, Y.H.[Yong-Hao],
Hua, G.[Gang],
End-to-end pedestrian trajectory prediction via Efficient Multi-modal
Predictors,
CVIU(248), 2024, pp. 104107.
Elsevier DOI
2409
Pedestrian trajectory prediction, Multimodal prediction, Parallel predictors
BibRef
Gawande, U.[Ujwalla],
Hajari, K.[Kamal],
Golhar, Y.[Yogesh],
Deep learning approach to pedestrian detection and path prediction,
IJCVR(14), No. 6, 2024, pp. 693-714.
DOI Link
2410
BibRef
Uhlemann, N.[Nico],
Fent, F.[Felix],
Lienkamp, M.[Markus],
Evaluating Pedestrian Trajectory Prediction Methods With Respect to
Autonomous Driving,
ITS(25), No. 10, October 2024, pp. 13937-13946.
IEEE DOI
2410
Trajectory, Pedestrians, History, Predictive models, Neural networks,
Measurement, Benchmark testing, Autonomous vehicles, runtime
BibRef
Feng, A.[Ang],
Han, C.[Cheng],
Gong, J.[Jun],
Yi, Y.[Yang],
Qiu, R.Q.[Rui-Qi],
Cheng, Y.[Yang],
Multi-Scale Learnable Gabor Transform for Pedestrian Trajectory
Prediction From Different Perspectives,
ITS(25), No. 10, October 2024, pp. 13253-13263.
IEEE DOI
2410
Trajectory, Pedestrians, Transforms, Predictive models,
Time-frequency analysis, Task analysis, Computer architecture,
multi-scale feature dimension enhancement module
BibRef
Ling, Y.C.[Yan-Cheng],
Ma, Z.[Zhenliang],
Zhang, Q.[Qi],
Xie, B.Q.[Bang-Quan],
Weng, X.X.[Xiao-Xiong],
PedAST-GCN: Fast Pedestrian Crossing Intention Prediction Using
Spatial-Temporal Attention Graph Convolution Networks,
ITS(25), No. 10, October 2024, pp. 13277-13290.
IEEE DOI
2410
Pedestrians, Predictive models, Data models, Feature extraction,
Computational modeling, Skeleton, Data mining,
video image data
BibRef
Liu, H.[Hui],
Liu, C.S.[Chun-Sheng],
Chang, F.[Faliang],
Lu, Y.[Yansha],
Liu, M.[Minhang],
Egocentric Vulnerable Road Users Trajectory Prediction With
Incomplete Observation,
ITS(25), No. 10, October 2024, pp. 13694-13705.
IEEE DOI
2410
Trajectory, Feature extraction, Training, Roads, Pedestrians,
Long short term memory, Encoding, Incomplete observation,
feature fusion
BibRef
Wang, K.[Kehao],
Zou, H.[Han],
Social-ATPGNN: Prediction of multi-modal pedestrian trajectory of
non-homogeneous social interaction,
IET-CV(18), No. 7, 2024, pp. 907-921.
DOI Link
2411
convolutional neural nets
BibRef
Khel, M.H.K.[Muhammad Haris Kaka],
Greaney, P.[Paul],
McAfee, M.[Marion],
Moffett, S.[Sandra],
Meehan, K.[Kevin],
GSTGM: Graph, spatial-temporal attention and generative based model
for pedestrian multi-path prediction,
IVC(151), 2024, pp. 105245.
Elsevier DOI
2411
Pedestrians trajectory, Multipaths,
Graph convolutional network, Attentions, Computer vision
BibRef
Wang, H.M.[Hong-Mei],
Xing, S.[Sheng],
Wang, Z.W.[Zhi-Wei],
Min, M.H.[Ming-Hui],
Li, S.Y.[Shi-Yin],
Multi-System Fusion Positioning Method Based on Factor Graph,
SPLetters(31), 2024, pp. 3025-3029.
IEEE DOI
2411
Location awareness, Jacobian matrices, Sensors, Trajectory, Accuracy,
Pedestrians, Mathematical models, Sensor systems, multi-system fusion
BibRef
Dong, Y.H.[Yong-Hao],
Wang, L.[Le],
Zhou, S.P.[San-Ping],
Hua, G.[Gang],
Sun, C.Y.[Chang-Yin],
Sparse Pedestrian Character Learning for Trajectory Prediction,
MultMed(26), 2024, pp. 11070-11082.
IEEE DOI
2412
Trajectory, Pedestrians, Predictive models, Cameras, Degradation,
Accuracy, Long short term memory,
sparse pedestrian character learning
BibRef
Li, L.H.[Lin-Hui],
Lin, X.T.[Xiao-Tong],
Huang, Y.[Yejia],
Zhang, Z.Z.[Zi-Zhen],
Hu, J.F.[Jian-Fang],
Beyond Minimum-of-N: Rethinking the Evaluation and Methods of
Pedestrian Trajectory Prediction,
CirSysVideo(34), No. 12, December 2024, pp. 12880-12893.
IEEE DOI
2501
Trajectory, Measurement, Predictive models, Pedestrians,
Computational modeling, Accuracy, MoN-ADE
BibRef
Mei, L.[Ling],
Fu, M.Y.[Ming-Yu],
Wang, B.J.[Bing-Jie],
Jia, L.X.[Lv-Xiang],
Yu, M.Y.[Ming-Yu],
Zhang, Y.[Yu],
Zhang, L.J.[Li-Jun],
LSN-GTDA: Learning Symmetrical Network via Global Thermal Diffusion
Analysis for Pedestrian Trajectory Prediction in Unmanned Aerial
Vehicle Scenarios,
RS(17), No. 1, 2025, pp. 154.
DOI Link
2501
BibRef
Lin, X.T.[Xiao-Tong],
Liang, T.M.[Tian-Ming],
Lai, J.H.[Jian-Huang],
Hu, J.F.[Jian-Fang],
Progressive Pretext Task Learning for Human Trajectory Prediction,
ECCV24(XXX: 197-214).
Springer DOI
2412
BibRef
Lee, S.J.[Seong-Ju],
Lee, J.[Junseok],
Yu, Y.[Yeonguk],
Kim, T.[Taeri],
Lee, K.[Kyoobin],
Mart: Multiscale Relational Transformer Networks for Multi-agent
Trajectory Prediction,
ECCV24(LXVI: 89-107).
Springer DOI
2412
BibRef
Wang, Y.[Yufu],
Wang, Z.Y.[Zi-Yun],
Liu, L.J.[Ling-Jie],
Daniilidis, K.[Kostas],
Tram: Global Trajectory and Motion of 3d Humans from in-the-wild Videos,
ECCV24(XI: 467-487).
Springer DOI
2412
BibRef
Thakkar, N.[Neerja],
Mangalam, K.[Karttikeya],
Bajcsy, A.[Andrea],
Malik, J.[Jitendra],
Adaptive Human Trajectory Prediction via Latent Corridors,
ECCV24(XXXVIII: 297-314).
Springer DOI
2412
BibRef
Zhang, L.[Liang],
Xu, N.[Nathaniel],
Yang, P.F.[Peng-Fei],
Jin, G.[Gaojie],
Huang, C.C.[Cheng-Chao],
Zhang, L.J.[Li-Jun],
TrajPAC: Towards Robustness Verification of Pedestrian Trajectory
Prediction Models,
ICCV23(8293-8305)
IEEE DOI
2401
BibRef
Wang, J.B.[Jing-Bo],
Luo, Z.Y.[Zheng-Yi],
Yuan, Y.[Ye],
Li, Y.X.[Yi-Xuan],
Dai, B.[Bo],
PACER+: On-Demand Pedestrian Animation Controller in Driving
Scenarios,
CVPR24(718-728)
IEEE DOI
2410
Pedestrians, Tracking, Diversity reception, Controllability,
Animation, Trajectory
BibRef
Bae, I.[Inhwan],
Lee, J.[Junoh],
Jeon, H.G.[Hae-Gon],
Can Language Beat Numerical Regression? Language-Based Multimodal
Trajectory Prediction,
CVPR24(753-766)
IEEE DOI Code:
WWW Link.
2410
Pedestrians, Computational modeling, Stochastic processes,
Predictive models, Multitasking, Data models, Cognition,
multi agent
BibRef
Lin, H.Z.[Hao-Zhe],
Wei, C.Y.[Chun-Yu],
He, L.[Li],
Guo, Y.C.[Yu-Chen],
Zhao, Y.Q.[Yun-Qi],
Li, S.[Shanglong],
Fang, L.[Lu],
GigaTraj: Predicting Long-term Trajectories of Hundreds of
Pedestrians in Gigapixel Complex Scenes,
CVPR24(19331-19340)
IEEE DOI
2410
Bridges, Pedestrians, Annotations, Computational modeling, Semantics,
Predictive models
BibRef
Li, H.[He],
Ye, M.[Mang],
Zhang, M.[Ming],
Du, B.[Bo],
All in One Framework for Multimodal Re-Identification in the Wild,
CVPR24(17459-17469)
IEEE DOI Code:
WWW Link.
2410
Codes, Feature extraction, Trajectory, Data mining, Person ReID,
Multimodal Learning
BibRef
Wang, K.L.[Kuan-Lin],
Tsao, L.W.[Li-Wu],
Wu, J.C.[Jhih-Ciang],
Shuai, H.H.[Hong-Han],
Cheng, W.H.[Wen-Huang],
TrajFine: Predicted Trajectory Refinement for Pedestrian Trajectory
Forecasting,
WAD24(4483-4492)
IEEE DOI
2410
Training, Pedestrians, Force, Predictive models, Benchmark testing,
Trajectory Prediction, Diffusion Model
BibRef
Kim, S.[Sungjune],
Chi, H.G.[Hyung-Gun],
Lim, H.[Hyerin],
Ramani, K.[Karthik],
Kim, J.[Jinkyu],
Kim, S.[Sangpil],
Higher-order Relational Reasoning for Pedestrian Trajectory
Prediction,
CVPR24(15251-15260)
IEEE DOI
2410
Pedestrians, Accuracy, Aggregates, Predictive models, Cognition,
Social factors, Trajectory, Trajectory Prediction, Graph Convolutional Network
BibRef
Shi, L.S.[Liu-Shuai],
Wang, L.[Le],
Zhou, S.P.[San-Ping],
Hua, G.[Gang],
Trajectory Unified Transformer for Pedestrian Trajectory Prediction,
ICCV23(9641-9650)
IEEE DOI
2401
BibRef
Damirchi, H.[Haleh],
Greenspan, M.[Michael],
Etemad, A.[Ali],
Context-Aware Pedestrian Trajectory Prediction with Multimodal
Transformer,
ICIP23(2535-2539)
IEEE DOI
2312
BibRef
Jeon, Y.[Yuntae],
Tran, D.Q.[Dai Quoc],
Park, M.S.[Min-Soo],
Park, S.[Seunghee],
Leveraging Future Trajectory Prediction for Multi-Camera People
Tracking,
AICity23(5399-5408)
IEEE DOI
2309
BibRef
Gu, J.[Junru],
Hu, C.X.[Chen-Xu],
Zhang, T.Y.[Tian-Yuan],
Chen, X.[Xuanyao],
Wang, Y.L.[Yi-Lun],
Wang, Y.[Yue],
Zhao, H.[Hang],
ViP3D: End-to-End Visual Trajectory Prediction via 3D Agent Queries,
CVPR23(5496-5506)
IEEE DOI
2309
BibRef
Xu, Y.[Yi],
Bazarjani, A.[Armin],
Chi, H.G.[Hyung-Gun],
Choi, C.[Chiho],
Fu, Y.[Yun],
Uncovering the Missing Pattern: Unified Framework Towards Trajectory
Imputation and Prediction,
CVPR23(9632-9643)
IEEE DOI
2309
BibRef
Bae, I.[Inhwan],
Park, J.H.[Jin-Hwi],
Jeon, H.G.[Hae-Gon],
Learning Pedestrian Group Representations for Multi-modal Trajectory
Prediction,
ECCV22(XXII:270-289).
Springer DOI
2211
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, 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.L.[Tian-Lu],
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 -- Human Motion, Surveillance, Tracking, Surveillance, Activities continues in
Crowds, Tracking Multiple People, Multiple Pedestrian Tracking .