The DOI matches its Crossref record; 73 of the 74 references it cites were found in a registry or an index.
Learning to Track with Object Permanence
Open accessAbstract
Tracking by detection, the dominant approach for online multi-object tracking, alternates between localization and association steps. As a result, it strongly depends on the quality of instantaneous observations, often failing when objects are not fully visible. In contrast, tracking in humans is underlined by the notion of object permanence: once an object is recognized, we are aware of its physical existence and can approximately localize it even under full occlusions. In this work, we introduce an end-to-end trainable approach for joint object detection and tracking that is capable of such reasoning. We build on top of the recent CenterTrack architecture, which takes pairs of frames as input, and extend it to videos of arbitrary length. To this end, we augment the model with a spatio-temporal, recurrent memory module, allowing it to reason about object locations and identities in the current frame using all the previous history. It is, however, not obvious how to train such an approach. We study this question on a new, large-scale, synthetic dataset for multi-object tracking, which provides ground truth annotations for invisible objects, and propose several approaches for supervising tracking behind occlusions. Our model, trained jointly on synthetic and real data, outperforms the state of the art on KITTI and MOT17 datasets thanks to its robustness to occlusions.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“However, the projection of non-linear 3D motion [50] into the 2D image domain still poses a challenging problem for many models.”
From TrackFormer: Multi-Object Tracking with Transformers (2022) · cites this paper as [50]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Only [50,53,54] which follow [66] and pretrain on 6 additional tracking datasets (6M) surpass our performance.”
From TrackFormer: Multi-Object Tracking with Transformers (2022) · cites this paper as [50]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.324 · Cited by 30,581
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545
Are we ready for autonomous driving? The KITTI vision benchmark suite
Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248074 · Cited by 14,897
nuScenes: A Multimodal Dataset for Autonomous Driving
Caesar, H., Bankiti, V., Lang, A. H., Vora, S., Liong, V. E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., & Beijbom, O. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01164 · Cited by 7,152
Unsupervised Domain Adaptation by BackpropagationPreprint
Ganin, Y., & Lempitsky, V. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1409.7495 · Cited by 5,441
Simple online and realtime tracking with a deep association metric
Wojke, N., Bewley, A., & Paulus, D. (2017) · 2017 IEEE International Conference on Image Processing (ICIP) · doi:10.1109/icip.2017.8296962 · Cited by 5,286
Backpropagation through time: what it does and how to do it
Werbos, P. J. (1990) · Proceedings of the IEEE · doi:10.1109/5.58337 · Cited by 4,879
Research works printed but not linked: 1
- 2021, Parallel domain
Cited by
Sorted by citations
Items 101–200 of 226
SSAT: Self-Supervised Associating Network for Multiobject Tracking
Chung, T.-Y., Cho, M., Lee, H.-S., & Lee, S. (2022) · IEEE transactions on circuits and systems for video technology (Print) · doi:10.1109/tcsvt.2022.3186751 · Cited by 10
Where is my Wallet? Modeling Object Proposal Sets for Egocentric Visual Query Localization
Xu, M., Li, Y., Fu, C.-Y., Ghanem, B., Xiang, T., & Pérez-Rúa, J.-M. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00255 · Cited by 18
Multiple Pedestrian Tracking in Dense Crowds Combined with Head Tracking
Qi, Z., Zhou, M., Zhu, G., & Xue, Y. (2022) · Applied Sciences · doi:10.3390/app13010440 · Cited by 8
Multi-object tracking by multi-feature fusion to associate all detected boxes
Bilakeri, S., & Karunakar, A. K. (2022) · Cogent Engineering · doi:10.1080/23311916.2022.2151553 · Cited by 8
FRoG-MOT: Fast and Robust Generic Multiple-Object Tracking by IoU and Motion-State Associations
Ogawa, T., Shibata, T., & Hosoi, T. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00643 · Cited by 9
G2EMOT: Guided Embedding Enhancement for Multiple Object Tracking in Complex Scenes
Ma, J., Wu, F., Li, C., Tang, C., Zhang, J., Xu, Z., Li, M., & Liu, D. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3449952 · Cited by 8
Automation of Computer Vision Applications for Real-time Combat Sports Video Analysis
Quinn, E., & Corcoran, N. (2022) · European Conference on the Impact of Artificial Intelligence and Robotics · doi:10.34190/eciair.4.1.930 · Cited by 8
Improving Multiple Pedestrian Tracking in Crowded Scenes with Hierarchical Association
Xiao, C., & Luo, Z. (2023) · Entropy · doi:10.3390/e25020380 · Cited by 9
Rlm-tracking: online multi-pedestrian tracking supported by relative location mapping
Ren, K., Hu, C., & Xi, H. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-023-02070-7 · Cited by 7
An end-to-end tracking framework via multi-view and temporal feature aggregation
Yang, Y., Xu, M., Ralph, J. F., Ling, Y., & Pan, X. (2024) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2024.104203 · Cited by 8
PMTrack: Multi-object Tracking with Motion-Aware
Guo, X., Zheng, Y., & Wang, D. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0960-4_26 · Cited by 9
Chen, Y., Wang, J., Zhou, Q., & Hu, H. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3543018 · Cited by 8
A review of object tracking based on deep learning
Zhao, G., Meng, F., Yang, C., Wei, H., Zhang, D., & Zheng, Z. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130988 · Cited by 7
Multiple Object Tracking Based on Occlusion-Aware Embedding Consistency Learning
Hu, Y., Niu, A., Zhu, Y. M., Yan, Q., Sun, J., & Zhang, Y. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446647 · Cited by 7
Wei, H., & Huang, Y. (2022) · Machines · doi:10.3390/machines10080668 · Cited by 7
Learning from Outputs: Improving Multi-Object Tracking Performance by Tracker Fusion
Scarrica, V. M., & Staiano, A. (2024) · Technologies · doi:10.3390/technologies12120239 · Cited by 7
Multi-object tracking in surveillance scenarios based on joint detection and multi-feature fusion
Zhao, R., Gong, J., & Wang, Z. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07381-w · Cited by 6
ConsistencyTrack: A robust multi-object tracker with a generation strategy of consistency model
Jiang, L., Wang, Z., Yin, S., Ma, G., Zhang, P., & Wu, B. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.112225 · Cited by 7
Multi-Stage Cross-Modality Feature Interaction for RGB-Thermal Multi-Object Tracking
Ma, J., Luo, H., Niu, S., Zhao, P., Liu, Y., Wei, Y., & Zhang, J. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3612499 · Cited by 6
Similarity- and Quality-Guided Relation Learning for Joint Detection and Tracking
Feng, W., Bai, L., Yao, Y., Gan, W., Wu, W., & Ouyang, W. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3279670 · Cited by 6
Adaptive probabilistic multi-model framework for robust 3D multi-object tracking under uncertainty
Gohari, E. E., Ramezani, R., & Baraani-Dastjerdi, A. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.126719 · Cited by 6
Voudouris, K., Slater, B., Cheke, L. G., Schellaert, W., Hernández‐Orallo, J., Halina, M., Patel, M., Alhas, I., Mecattaf, M. G., Burden, J., et al. (2025) · Behavior Research Methods · doi:10.3758/s13428-025-02616-3 · Cited by 16
An Effectively Finite-Tailed Updating for Multiple Object Tracking in Crowd Scenes
Xu, B., Liang, D., Li, L., Quan, R., & Zhang, M. (2022) · Applied Sciences · doi:10.3390/app12031061 · Cited by 6
Gladkova, M., Korobov, N., Demmel, N., Ošep, A., Leal-Taixé, L., & Cremers, D. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981260 · Cited by 6
SPRTracker: Learning Spatial-Temporal Pixel Aggregations for Multiple Object Tracking
Liu, J., Kong, J., Jiang, M., & Liu, T. (2022) · IEEE Signal Processing Letters · doi:10.1109/lsp.2023.3236262 · Cited by 6
CSMOT: Make One-Shot Multi-Object Tracking in Crowded Scenes Great Again
Hou, H., Shen, C., Zhang, X., & Gao, W. (2023) · Sensors · doi:10.3390/s23073782 · Cited by 6
OsaMOT: Occlusion and scale‐aware multi‐object tracking algorithm for low viewpoint
Yue, Y., Xu, D., He, K., Shi, H., & Zhang, H. (2021) · IET Image Processing · doi:10.1049/ipr2.12378 · Cited by 6
Multi-Object Tracking by Hierarchical Visual Representations
Cao, J., Pang, J., & Kitani, K. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611201 · Cited by 5
Joint Object Detection and Multi-Object Tracking Based on Hypergraph Matching
Cui, Z., Dai, Y., Duan, Y., & Tao, X. (2024) · Applied Sciences · doi:10.3390/app142311098 · Cited by 5
Spatial Cognition from Egocentric Video: Out of Sight, Not Out of Mind
Plizzari, C., Goel, S., Perrett, T., Chalk, J., Kanazawa, A., & Damen, D. (2025) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv66043.2025.00115 · Cited by 18
Lei, M., Song, Y., Zhao, J., Wang, X., Lyu, J., Xu, J., & Yan, W. (2022) · Sensors · doi:10.3390/s22228693 · Cited by 7
He, B., Yuan, L., & Lv, K. (2024) · IEEE Signal Processing Letters · doi:10.1109/lsp.2024.3371331 · Cited by 5
Min, Z., Hassan, G. M., & Jo, G. (2024) · IEEE Access · doi:10.1109/access.2024.3432156 · Cited by 5
AM-SORT: Adaptable Motion Predictor with Historical Trajectory Embedding for Multi-Object Tracking
Kim, V., Jung, G., & Lee, S. (2025) · Lecture notes in computer science · doi:10.1007/978-981-97-8702-9_7 · Cited by 5
S2F2: Single-Stage Flow Forecasting for Future Multiple Trajectories Prediction
Chen, Y., Yang, H.-K., Chiu, C.-C., & Lee, C. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00285 · Cited by 5
OrbitTrack: Advanced RSO detection and tracking from wide field-of-view on-orbit images
Jeong, Y., Suthakar, V., Qashoa, R., Sohn, G., & Lee, R. S. K. (2025) · Advances in Space Research · doi:10.1016/j.asr.2025.08.006 · Cited by 4
Wang, C., Wu, Z., Ke, W., & Xiong, Z. (2024) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2024.104144 · Cited by 4
Single-Task Joint Learning Model for an Online Multi-Object Tracking Framework
Wang, Y.-K., Pan, T.-M., & Hu, C.-E. (2024) · Applied Sciences · doi:10.3390/app142210540 · Cited by 4
Yang, J., Feng, D., Gao, Y., & Liu, C. (2025) · Big Data Mining and Analytics · doi:10.26599/bdma.2025.9020024 · Cited by 5
CrossTracker: Robust Multi-Modal 3D Multi-Object Tracking via Cross Correction
Gu, L., Yan, X., Wang, W., Chen, H., Zhu, D., Nan, L., & Wei, M. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3601667 · Cited by 4
RFID-assisted visual multiple object tracking without using visual appearance and motion
Song, R., Wang, Z., Guo, J. Q., Han, B. S., Wong, A. H. Y., Сун, Л., & Lin, Z. (2023) · Proceedings - International Conference on Image Processing · doi:10.1109/icip49359.2023.10221992 · Cited by 4
Learning key lines for multi-object tracking
Li, Y., Ji, H., Chen, X., Yang, Y., & Lai, Y. (2024) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2024.103973 · Cited by 4
A high-quality trajectory generation method for the multi-person tracking
Ni, Z., Zhai, C., Miao, Z., & Li, Y. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109392 · Cited by 4
OneTrack: Demystifying the Conflict Between Detection and Tracking in End-to-End 3D Trackers
Wang, Q., He, J., Chen, Y., & Zhang, Z. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72667-5_22 · Cited by 4
ERMOT: Evidence Reasoning-Based Robust Multiple Object Tracking Method
Xu, X., Wang, X., Wu, F., Zhang, Z., & Chang, L. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3488786 · Cited by 4
FusionTrack: An Online 3D Multi-object Tracking Framework Based on Camera-LiDAR Fusion
Zeng, W., Fan, J., Tian, X., Chu, H., & Gao, B. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10802805 · Cited by 4
GroupTrack: Multi-Object Tracking by Using Group Motion Patterns
Xu, X., Ren, W., Sun, G., Ji, H., Gao, Y., & Liu, H. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10802541 · Cited by 4
Real-Time Monitoring of Mobile Construction Resources Based on Multiple Object Tracking
Deng, R., Wang, K., & Mao, Y. (2025) · Journal of Computing in Civil Engineering · doi:10.1061/jccee5.cpeng-6587 · Cited by 5
Negative-Driven Training Pipeline for Siamese Visual Tracking
Yang, X. H., Zhao, C., Yang, J., Song, Y., & Zhao, Y. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3323134 · Cited by 5
Object Permanence Filter for Robust Tracking with Interactive Robots
Peng, S., Wang, M. X., Shah, J., & Figueroa, N. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611528 · Cited by 4
Real-Time Object Behavior Prediction Based on LiDAR Suitable for Urban Autonomous Driving
Jeong, J., & Kee, S.-C. (2022) · Transactions of Korean Society of Automotive Engineers · doi:10.7467/ksae.2022.30.7.589 · Cited by 4
Wang, W., Lin, Z., Huang, M., Hu, S., Jin, T., Li, T., Wan, M., Gu, J. J., & Zhu, S. (2022) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio55434.2022.10011698 · Cited by 4
ReferGPT: Towards Zero-Shot Referring Multi-Object Tracking
Chamiti, T., Bella, L. D., Munteanu, A., & Deligiannis, N. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00370 · Cited by 3
Pham, D. T., Thủy, N. T. T., & Tran, L. Q. (2025) · Machine Vision and Applications · doi:10.1007/s00138-025-01756-y · Cited by 4
Multi-object tracking using context-sensitive enhancement via feature fusion
Zhou, Y., Chen, J., Wang, D., & Zhu, X. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16027-z · Cited by 3
Offline Tracking with Object Permanence
Liu, X., & Caesar, H. (2024) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv55156.2024.10588572 · Cited by 3
Hu, W., Wang, S., Zhou, Z., Gao, J. G., Li, Y., & Maybank, S. J. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3457886 · Cited by 3
Multi-Object Tracking Algorithm of Fusing Trajectory Compensation
Jin, J., Wang, L., You, Q., & Sun, J. (2022) · Mathematics · doi:10.3390/math10152606 · Cited by 3
Mask Guided Spatial-Temporal Fusion Network for Multiple Object Tracking
Zhao, S., Wu, Y., Wang, S., Ke, W., & Sheng, H. (2022) · Proceedings - International Conference on Image Processing · doi:10.1109/icip46576.2022.9898054 · Cited by 3
TLtrack: Combining Transformers and a Linear Model for Robust Multi-Object Tracking
He, Z., Zhao, K., & Zeng, D. (2024) · AI · doi:10.3390/ai5030047 · Cited by 3
Visual Perception System for Autonomous Driving
Zhang, Q., Gou, S., & Li, W. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10802028 · Cited by 4
Metrics for Machine Learning Models to Facilitate SOTIF Analysis in Autonomous Vehicles
Madala, K., & Gonzalez, C. A. (2023) · SAE International Journal of Advances and Current Practices in Mobility · doi:10.4271/2023-01-0829 · Cited by 4
BAM-SORT: border-guided activated matching for online multi-object tracking
Chao, Y., Zhu, H., & Lu, H. (2025) · Applied Intelligence · doi:10.1007/s10489-024-06037-1 · Cited by 2
Uncertain Object Representation for Image-Based 3D Object Perception
Wang, Q., Chen, Y., & Zhang, Z. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3568120 · Cited by 2
Tracking-Assisted Object Detection with Event Cameras
Yen, T.-K., Morawski, I., Dangi, S., He, K., Lin, C.-Y., Yeh, J.-F., Su, H.-T., & Hsu, W. H. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-92460-6_9 · Cited by 3
Cao, M., Wang, C., Zhao, W., & Zhang, Z. (2025) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2025.3581444 · Cited by 2
Jia, Y., Zhang, Y., Zhou, C., & Yang, Y. (2023) · Displays · doi:10.1016/j.displa.2023.102488 · Cited by 2
Online Multiple Object Tracking Using Min-Cost Flow on Temporal Window for Autonomous Driving
Wei, H., Huang, Y., Zhang, Q., & Guo, Z. (2023) · World Electric Vehicle Journal · doi:10.3390/wevj14090243 · Cited by 2
SMP-Track: SAM in Multi-Pedestrian Tracking
Wang, S., Liu, H., Wang, Q., Zhou, Y., & Yao, Y. (2024) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa61799.2024.10722829 · Cited by 2
Pseudo-LiDAR With Two-Dimensional Instance for Monocular Three-Dimensional Object Tracking
Gao, R. Q., Kim, J., Chu, P. M., & Cho, K. (2025) · IEEE Access · doi:10.1109/access.2025.3549790 · Cited by 2
Adaptive and extended trajectory matching for robust multi-target tracking
Wang, S., Guo, X., Deng, L., Sun, G., & Dong, M. (2025) · The Visual Computer · doi:10.1007/s00371-025-03872-2 · Cited by 2
DETrack: Multi-Object Tracking Algorithm Based on Feature Decomposition and Feature Enhancement
Wen, F., Huang, H., Yin, X., Ma, J., & Hu, X. (2024) · IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences · doi:10.1587/transfun.2023eap1162 · Cited by 1
SCMOT: Improving 3D Multi-Object Tracking via Semantic Inference and Confidence Optimization
Zhao, L., Wang, M., & Yue, Y. (2024) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc62350.2024.10587787 · Cited by 1
Autoregressive Temporal Modeling for Advanced Tracking-by-Diffusion
Nguyen, P., Madhok, R., Raj, B., & Luu, K. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02439-x · Cited by 1
Multi Object Tracking and Panoptic Segmentation in Monocular Birds Eye View Images
Muresan, M. P., & Nedevschi, S. (2025) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv64158.2025.11097596 · Cited by 1
TCGM: Spatiotemporal Clues for Multiobject Tracking via Topological Chronology Graph Neural Network
Qin, W., Quan, H., Kong, X., Xu, H., Pan, C., & Dong, X. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3600638 · Cited by 1
Huang, F.-K., Xu, H.-W., Lee, C., Tu, H.-Y., Shuai, H.-H., & Cheng, W.-H. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3758189 · Cited by 2
Edge-Graph Enhanced Network for Multi-Object Tracking in UAV Videos
Xu, Y., Hongbing, J. I., & Zhang, Y. (2026) · Remote Sensing · doi:10.3390/rs18060936 · Cited by 1
Multi-Object Tracking Based on Deep Path Aggregation Network
Chen, X., & Zhang, Y. (2022) · International Conference on Image, Vision and Computing (ICIVC) · doi:10.1109/icivc55077.2022.9886718 · Cited by 1
Dynamic trajectory quantification strategy for multiple object tracking with feature rearrangement
Zhang, Y., Tian, Q., Liu, T., & Kong, J. (2022) · Journal of Electronic Imaging · doi:10.1117/1.jei.31.6.063025 · Cited by 1
A Simple Baseline for Open-World Tracking via Self-training
Wang, B., Li, T., Wu, J., Jiang, Y., Lu, H., & He, Y. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3611695 · Cited by 1
Maximov, M., Meinhardt, T., Papakipos, Z., Hazırbaş, C., Canton, C., & Leal-Taixé, L. (2024) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg59268.2024.10581949 · Cited by 1
3D Multi-Object Tracking Employing MS-GLMB Filter for Autonomous Driving
Van, L. N., Hussain, M. I., Yow, K., & Jeon, M. (2024) · International Conference on Control, Automation and Information Sciences (ICCAIS) · doi:10.1109/iccais63750.2024.10814505 · Cited by 1
Instance-Wise Contrastive Learning for Multi-object Tracking
Luo, Q., & Xu, C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-18916-6_52 · Cited by 1
Adaptive Multi-person Tracking Method considering Appearance Changes
Jeon, D. H., & Chang, J. Y. (2023) · Journal of Broadcast Engineering · doi:10.5909/jbe.2023.28.6.698 · Cited by 1
First Steps Towards 3D Pedestrian Detection and Tracking from Single Image
Mancusi, G., Fabbri, M., Egidi, S., Verasani, M., Scarabelli, P., Calderara, S., & Cucchiara, R. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-06430-2_28
S2F2: Single-Stage Flow Forecasting for Future Multiple Trajectories Prediction
Chen, Y., Yang, H.-K., Chiu, C.-C., & Lee, C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20047-2_35
Object Permanence in Object Detection Leveraging Temporal Priors at Inference Time
Fürst, M., Bhugra, P., Schuster, R., & Stricker, D. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956426
PreTracker: Using Heatmap Prediction Combined with Feature to Enhance Tracking
Fan, F., He, Z., & Niu, K. (2022) · IEEE International Conference on Computer and Communications (ICCC) · doi:10.1109/iccc56324.2022.10065906
Pigtrack: A Novel Benchmark for Multi Object TrackingPreprint
Cao, G., Peng, D., Wei, W., Mao, X.-L., Fu, Y., & Chen, D. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4500700
Motion-Guided Spatial Perception for Enhanced Multi-Object Tracking: A Hierarchical FrameworkPreprint
Zheng, Y., Qi, H., Li, L., Li, S., Huang, Y., He, C., & Wang, D. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4504152
Cao, X., Zheng, Y., Yao, Y., Qin, H., Cao, X., & Guo, S. (2023) · doi:10.36227/techrxiv.24071700
Online Geometry Feature Tracker by Adaptive Cascade
Sun, Q., & Chai, L. (2023) · International Symposium on Computer Engineering and Intelligent Communications (ISCEIC) · doi:10.1109/isceic59030.2023.10271112
Block-Matching Multi-pedestrian Tracking
Zhang, C. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-8067-3_9
Cascaded-Scoring Tracklet Matching for Multi-object Tracking
Xie, Y., Wang, H., & Yang, L. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-8549-4_14
Multi-step Ahead Visual Trajectory Prediction for Object Tracking using Echo State Networks
Manibardo, E. L., Laña, I., & Ser, J. D. (2023) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc57777.2023.10422485
Wen, C., Gao, Y., & Li, J. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446257
Self-citationVisual multi-object tracking by interaction network
Irampaye, R., Chen, J., Zhu, X., & Zhou, Y. (2024) · International Symposium on Autonomous Systems (ISAS) · doi:10.1109/isas61044.2024.10552490
Multi-object tracking using adaptive-IoU loss and hierarchical association
Guo, W. Q., Liu, Q., & Ding, X. (2024) · Journal of Image and Graphics · doi:10.11834/jig.230390
A review of pedestrian trajectory tracking based on deep learning
Yu, R., Zhang, Z., Lu, Y., Song, W., Qin, X., Yi, N., & Chen, J. (2024) · International Conference on Image Processing and Artificial Intelligence (ICIPAI) · doi:10.1117/12.3035210
Metadata
- Authors
- Tokmakov, P., Li, J., Burgard, W., & Gaidon, A.
- Affiliations
- Toyota Motor Corporation (Switzerland) (CH)
- University of Freiburg (DE)
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 10840-10849
- OpenAlex
- W3142617676
- Cited by
- 226
- References
- 74
- Citations by year
- 2026: 14 · 2025: 43 · 2024: 72 · 2023: 48 · 2022: 46 · 2021: 3
- FWCI
- 13.74
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- IEEE/CVF International Conference on Computer Vision (ICCV)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- multi-target tracking, object permanence, occlusion handling, joint detection and tracking, spatiotemporal memory, end-to-end tracking, KITTI, MOT17
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
