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.
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The projection of non-linear 3D motion into the 2D image domain still poses a challenging problem for many models.
“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]
Section 3.3.2 says an occluded person's unexpected stop cannot be predicted without observations, supporting the point that non-linear motion challenges tracking. It also describes projecting estimated 3D positions into the camera frame, but does not say that the projection itself is a challenging problem for many models. (LLM, conf=0.82)
Experimental — accuracy not yet measuredOnly methods [50,53,54] that follow [66] and pretrain on 6 additional tracking datasets (6M) surpass TrackFormer's performance on MOT17 private detections.
“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]
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Simple Cues Lead to a Strong Multi-Object Tracker
Seidenschwarz, J., Brasó, G., Serrano, V. C., Elezi, I., & Leal-Taixé, L. (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/cvpr52729.2023.01327 · Cited by 129 · see citing works
Wang, K., Zhang, X., Qin, W., Li, X., Gao, J., Yang, L., Li, Z., Li, J., Zhu, L., Wang, H., et al. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3285651 · Cited by 124 · see citing works
Self-citationSparseTrack: Multi-Object Tracking by Performing Scene Decomposition Based on Pseudo-Depth
Liu, Z., Wang, X., Wang, C., Liu, W., & Bai, X. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3524670 · Cited by 126 · see citing works
Unifying Short and Long-Term Tracking with Graph Hierarchies
Cetintas, O., Brasó, G., & Leal-Taixé, L. (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/cvpr52729.2023.02191 · Cited by 107 · see citing works
Modelling Ambiguous Assignments for Multi-Person Tracking in Crowds
Stadler, D., & Beyerer, J. (2022) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw54805.2022.00019 · Cited by 106 · see citing works
SMILEtrack: SiMIlarity LEarning for Occlusion-Aware Multiple Object Tracking
Wang, Y.-H., Hsieh, J.-W., Chen, P.-Y., Chang, M., So, H.-H., & Li, X. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i6.28386 · Cited by 110 · see citing works
Focus On Details: Online Multi-Object Tracking with Diverse Fine-Grained Representation
Ren, H., Han, S., Ding, H., Zhang, Z., Wang, H., & Wang, F. (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/cvpr52729.2023.01086 · Cited by 97 · see citing works
UTM: A Unified Multiple Object Tracking Model with Identity-Aware Feature Enhancement
You, S., Yao, H., Bao, B., & Xu, C. (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/cvpr52729.2023.02095 · Cited by 94 · see citing works
MUTR3D: A Multi-camera Tracking Framework via 3D-to-2D Queries
Zhang, T., Chen, X., Wang, Y., Wang, Y., & Zhao, H. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00500 · Cited by 83 · see citing works
Looking Beyond Two Frames: End-to-End Multi-Object Tracking Using Spatial and Temporal Transformers
Zhu, T., Hiller, M., Ehsanpour, M., Ma, R., Drummond, T. W., Reid, I. D., & Rezatofighi, H. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3213073 · Cited by 82 · see citing works
DiffusionTrack: Diffusion Model for Multi-Object Tracking
Luo, R., Song, Z., Ma, L., Wei, J., Yang, W., & Yang, M. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i5.28192 · Cited by 80 · see citing works
Standing Between Past and Future: Spatio-Temporal Modeling for Multi-Camera 3D Multi-Object Tracking
Pang, Z., Li, J., Tokmakov, P., Chen, D., Zagoruyko, S., & Wang, Y.-X. (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/cvpr52729.2023.01719 · Cited by 66 · see citing works
Self-citationTripletTrack: 3D Object Tracking using Triplet Embeddings and LSTM
Marinello, N., Proesmans, M., & Gool, L. V. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00496 · Cited by 62 · see citing works
Tracking People by Predicting 3D Appearance, Location and Pose
Rajasegaran, J., Pavlakos, G., Kanazawa, A., & Malik, J. (2022) · 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/cvpr52688.2022.00276 · Cited by 62 · see citing works
Xue, H., Cao, Q., Ju, Y., Hu, H., Wang, H., Zhang, A., & Su, L. (2022) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/3560905.3568545 · Cited by 61 · see citing works
SimpleTrack: Rethinking and Improving the JDE Approach for Multi-Object Tracking
Li, J., Ding, Y., Wei, H., Zhang, Y., & Lin, W. (2022) · Sensors · doi:10.3390/s22155863 · Cited by 60 · see citing works
Self-citationWang, X., Sun, Z., Chehri, A., Jeon, G., & Song, Y. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102247 · Cited by 64 · see citing works
Cao, X., Zheng, Y., Yao, Y., Qin, H., Cao, X., & Guo, S. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3526066 · Cited by 58 · see citing works
Tracking Objects as Pixel-Wise Distributions
Zhao, Z., Wu, Z., Zhuang, Y., Li, B., & Jia, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20047-2_5 · Cited by 58 · see citing works
Nie, C., Ju, Z., Sun, Z., & Zhang, H. (2023) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2023.3259441 · Cited by 54 · see citing works
Multi-object tracking review: retrospective and emerging trend
Guan, Z., Wang, Z., Zhang, G., Li, L., Zhang, M., Shi, Z., & Jiang, N. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11212-y · Cited by 58 · see citing works
Dynamic center point learning for multiple object tracking under Severe occlusions
Hu, Y., Niu, A., Sun, J., Zhu, Y., Yan, Q., Dong, W., Woźniak, M., & Zhang, Y. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112130 · Cited by 49 · see citing works
PolarMOT: How Far Can Geometric Relations Take us in 3D Multi-object Tracking?
Kim, A., Brasó, G., Ošep, A., & Leal-Taixé, L. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20047-2_3 · Cited by 47 · see citing works
A Multi-Object Tracking Algorithm With Center-Based Feature Extraction and Occlusion Handling
Cao, Z., Li, J., Zhang, D., Zhou, M., & Abusorrah, A. M. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3229978 · Cited by 45 · see citing works
Self-citationFast re-OBJ: real-time object re-identification in rigid scenes
Bayraktar, E., Wang, Y., & Bue, A. D. (2022) · Machine Vision and Applications · doi:10.1007/s00138-022-01349-z · Cited by 41 · see citing works
Towards Generalizable Multi-Object Tracking
Qin, Z., Wang, L., Zhou, S., Fu, P., Hua, G., & Tang, W. (2024) · 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/cvpr52733.2024.01797 · Cited by 41 · see citing works
BPMTrack: Multi-Object Tracking With Detection Box Application Pattern Mining
Gao, Y., Xu, H., Li, J., & Gao, X. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3364828 · Cited by 39 · see citing works
Self-citationMulti-Object Tracking and Segmentation Via Neural Message Passing
Brasó, G., Cetintas, O., & Leal-Taixé, L. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01678-6 · Cited by 38 · see citing works
A Review of Multi‐Object Tracking in Recent Times
Li, S., Ren, H., Xie, X., & Cao, Y. (2025) · IET Computer Vision · doi:10.1049/cvi2.70010 · Cited by 39 · see citing works
Interactive Multi-Scale Fusion of 2D and 3D Features for Multi-Object Vehicle Tracking
Wang, G., Peng, C., Gu, Y., Zhang, J., & Wang, H. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3275954 · Cited by 34 · see citing works
Monocular 3D Multi-Object Tracking with an EKF Approach for Long-Term Stable Tracks
Reich, A., & Wuensche, H. (2021) · International Conference on Information Fusion (FUSION) · doi:10.23919/fusion49465.2021.9626850 · Cited by 33 · see citing works
SeaTrack: Rethinking Observation-Centric SORT for Robust Nearshore Multiple Object Tracking
Ding, J., Li, W., Yang, M., Zhao, Y., Pei, L., & Tian, A. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.111091 · Cited by 32 · see citing works
Delving into the Trajectory Long-tail Distribution for Muti-object Tracking
Chen, S., Yu, E., Li, J., & Tao, W. (2024) · 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/cvpr52733.2024.01830 · Cited by 31 · see citing works
Self-citationGenerative Camera Dolly: Extreme Monocular Dynamic Novel View Synthesis
Hoorick, B. V., Wu, R., Ozguroglu, E., Sargent, K., Liu, R., Tokmakov, P., Dave, A., Zheng, C., & Vondrick, C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72691-0_18 · Cited by 32 · see citing works
Self-citationMotionTrack: End-to-End Transformer-based Multi-Object Tracking with LiDAR-Camera Fusion
Zhang, C., Zhang, C., Guo, Y., Chen, L., & Happold, M. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00020 · Cited by 30 · see citing works
Interaction-Aware Labeled Multi-Bernoulli Filter
Ishtiaq, N., Gostar, A. K., Bab‐Hadiashar, A., & Hoseinnezhad, R. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3294519 · Cited by 29 · see citing works
Saraceni, L., Motoi, I. M., Nardi, D., & Ciarfuglia, T. A. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610231 · Cited by 28 · see citing works
One-Shot Multiple Object Tracking With Robust ID Preservation
Lv, W., Zhang, N., Zhang, J., & Zeng, D. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3339609 · Cited by 26 · see citing works
Contrastive Learning for Multi-Object Tracking with Transformers
Plaen, P.-F. D., Marinello, N., Proesmans, M., Tuytelaars, T., & Gool, L. V. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00672 · Cited by 26 · see citing works
Liu, C., Li, H., & Wang, Z. (2023) · International Journal of Computer Vision · doi:10.1007/s11263-023-01933-4 · Cited by 25 · see citing works
Lost and Found: Overcoming Detector Failures in Online Multi-object Tracking
Vaquero, L., Xu, Y., Alameda-Pineda, X., Brea, V. M., & Mucientes, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73464-9_27 · Cited by 26 · see citing works
Learning data association for multi-object tracking using only coordinates
Miah, M., Bilodeau, G.-A., & Saunier, N. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.111169 · Cited by 26 · see citing works
Hong, J., Wang, T., Han, Y., & Wei, T. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3539462 · Cited by 25 · see citing works
Multi-view Tracking Using Weakly Supervised Human Motion Prediction
Engilberge, M., Liu, W., & Fua, P. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00163 · Cited by 23 · see citing works
3D multi-object tracking based on informatic divergence-guided data association
He, J., Fu, C., Wang, X., & Wang, J. (2024) · Signal Processing · doi:10.1016/j.sigpro.2024.109544 · Cited by 22 · see citing works
Gao, Y., Xu, H., Li, J., Wang, N., & Gao, X. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i3.27953 · Cited by 21 · see citing works
Self-citationAOH: Online Multiple Object Tracking With Adaptive Occlusion Handling
Jiang, M., Zhou, C., & Kong, J. (2022) · IEEE Signal Processing Letters · doi:10.1109/lsp.2022.3191549 · Cited by 21 · see citing works
Tracking Game: Self-adaptative Agent based Multi-object Tracking
Wang, S., Yang, D., Wu, Y., Liu, Y., & Sheng, H. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3548231 · Cited by 21 · see citing works
UKF‐MOT: An unscented Kalman filter‐based 3D multi‐object tracker
Liu, M., Niu, J., & Liu, Y. (2024) · CAAI Transactions on Intelligence Technology · doi:10.1049/cit2.12315 · Cited by 21 · see citing works
Self-Supervised Multi-Object Tracking with Path Consistency
Lu, Z., Shuai, B., Chen, Y., Xu, Z., & Modolo, D. (2024) · 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/cvpr52733.2024.01799 · Cited by 20 · see citing works
Rethinking Joint Detection and Embedding for Multiobject Tracking in Multiscenario
Xu, L., & Huang, Y. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3366983 · Cited by 19 · see citing works
AHOR: Online Multi-Object Tracking With Authenticity Hierarchizing and Occlusion Recovery
Jin, H., Nie, X., Yan, Y., Chen, X., Zhu, Z., & Qi, D. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3392939 · Cited by 19 · see citing works
Cascaded matching based on detection box area for multi-object tracking
Gu, S., Zhang, M., Xiao, Q., & Shi, W. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112075 · Cited by 18 · see citing works
Motion-guided and occlusion-aware multi-object tracking with hierarchical matching
Zheng, Y., Qi, H., Li, L., Li, S., Huang, Y., He, C., & Wang, D. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110369 · Cited by 18 · see citing works
Robust Multi-object Tracking by Marginal Inference
Zhang, Y., Wang, C., Wang, X., Zeng, W., & Liu, W. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20047-2_2 · Cited by 17 · see citing works
STMT: Spatio-temporal memory transformer for multi-object tracking
Gu, S., Ma, J., Hui, G., Xiao, Q., & Shi, W. (2023) · Applied Intelligence · doi:10.1007/s10489-023-04617-1 · Cited by 17 · see citing works
Delving into Motion-Aware Matching for Monocular 3D Object Tracking
Huang, K., Yang, M., & Tsai, Y. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00636 · Cited by 16 · see citing works
Du, C., Lin, C., Jin, R., Chai, B., Yao, Y., & Su, S. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17983-2 · Cited by 19 · see citing works
Hong, J., Wang, T., Han, Y., Di, W., & Dong, T. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3551692 · Cited by 15 · see citing works
HybridTrack: A Hybrid Approach for Robust Multi-Object Tracking
Bella, L. D., Lyu, Y., Cornelis, B., & Munteanu, A. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3575311 · Cited by 19 · see citing works
Lightweight Indoor Multi-Object Tracking in Overlapping FOV Multi-Camera Environments
Jang, J., Seon, M.-J., & Choi, J. (2022) · Sensors · doi:10.3390/s22145267 · Cited by 15 · see citing works
Ma, S., Duan, S., Hou, Z., Yu, W., Pu, L., & Zhao, X. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123581 · Cited by 15 · see citing works
Multi-Object Tracking Based on a Novel Feature Image With Multi-Modal Information
An, Y., Wu, J., Cui, Y., & Hu, H. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2023.3259999 · Cited by 14 · see citing works
AttentionTrack: Multiple Object Tracking in Traffic Scenarios Using Features Attention
Zhang, C., Zheng, S., Wu, H., Gu, Z., Sun, W., & Yang, L. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3315222 · Cited by 14 · see citing works
CollabMOT Stereo Camera Collaborative Multi Object Tracking
Ninh, P. P., & Kim, H. (2024) · IEEE Access · doi:10.1109/access.2024.3356864 · Cited by 14 · see citing works
Nagy, M., Werghi, N., Hassan, B., Dias, J. M. M., & Khonji, M. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3581980 · Cited by 15 · see citing works
BGTracker: Cross-Task Bidirectional Guidance Strategy for Multiple Object Tracking
Zhou, C., Jiang, M., & Kong, J. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3256761 · Cited by 13 · see citing works
Towards Robust Reference System for Autonomous Driving: Rethinking 3D MOT
Wang, L., Zhang, J., Cai, P., & Li, X. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160645 · Cited by 14 · see citing works
Tracking Multiple Deformable Objects in Egocentric Videos
Huang, M., Li, X., Hu, J. F., Peng, H., & Lyu, S. (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/cvpr52729.2023.00147 · Cited by 13 · see citing works
Tracking Through Containers and Occluders in the Wild
Hoorick, B. V., Tokmakov, P., Stent, S., Li, J., & Vondrick, C. (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/cvpr52729.2023.01326 · Cited by 13 · see citing works
Self-citationA deep learning framework for multi‐object tracking in team sports videos
Cao, W., Wang, X., Liu, X., & Xu, Y.-S. (2024) · IET Computer Vision · doi:10.1049/cvi2.12266 · Cited by 14 · see citing works
OMCTrack: Integrating Occlusion Perception and Motion Compensation for UAV Multi-Object Tracking
Dang, Z., Sun, X., Sun, B., Guo, R., & Can, L. (2024) · Drones · doi:10.3390/drones8090480 · Cited by 16 · see citing works
Nagy, M., Khonji, M., Dias, J. M. M., & Javed, S. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160328 · Cited by 13 · see citing works
DeepSteg: Integerating new paradigms of cascaded deep video steganography for securing digital data
Magdy, S., Youssef, S. M., Fathalla, K. M., & ElShehaby, S. (2025) · Alexandria Engineering Journal · doi:10.1016/j.aej.2024.12.034 · Cited by 12 · see citing works
Hu, Y., Sun, J., Jin, H., Niu, A., Yan, Q., Zhu, Y., & Zhang, Y. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3558588 · Cited by 12 · see citing works
DeepScale: Online Frame Size Adaptation for Multi-object Tracking on Smart Cameras and Edge Servers
Nalaie, K., Xu, R., & Zheng, R. (2022) · IEEE/ACM International Conference on Internet-of-Things Design and Implementation (IoTDI) · doi:10.1109/iotdi54339.2022.00010 · Cited by 12 · see citing works
AIPT: Adaptive information perception for online multi-object tracking
Zhang, Y., Xie, H., Jia, Y., Meng, J., Sang, M., Qiu, J., Zhao, S., & Yang, Y. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111369 · Cited by 11 · see citing works
Multi-object tracking with adaptive measurement noise and information fusion
Huang, X., & Zhan, Y. (2024) · Image and Vision Computing · doi:10.1016/j.imavis.2024.104964 · Cited by 11 · see citing works
Revealing Occlusions with 4D Neural Fields
Hoorick, B. V., Tendulkar, P., Surís, D., Park, D., Stent, S., & Vondrick, C. (2022) · 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/cvpr52688.2022.00302 · Cited by 11 · see citing works
A systematic survey on recent deep learning-based approaches to multi-object tracking
Agrawal, H., Halder, A., & Chattopadhyay, P. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16910-9 · Cited by 11 · see citing works
Learnable Graph Matching: A Practical Paradigm for Data Association
He, J., Huang, Z., Wang, N., & Zhang, Z. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3362401 · Cited by 10 · see citing works
Zhou, T., Ye, Q., Luo, W., Ran, H., Shi, Z., & Chen, J. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02237-x · Cited by 11 · see citing works
Leveraging temporal-aware fine-grained features for robust multiple object tracking
Wu, H., Nie, J., Zhu, Z., He, Z., & Gao, M. (2022) · The Journal of Supercomputing · doi:10.1007/s11227-022-04776-x · Cited by 10 · see citing works
Prototype-guided Instance matching for multiple pedestrian tracking
Wang, Q., Zhang, W., Yang, W., Xu, C., & Cui, Z. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.03.068 · Cited by 10 · see citing works
APPTracker: Improving Tracking Multiple Objects in Low-Frame-Rate Videos
Zhou, T., Luo, W., Shi, Z., Chen, J., & Ye, Q. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3548162 · Cited by 10 · see citing works
Multi-object Tracking with Spatial-Temporal Tracklet Association
You, S., Yao, H., Bao, B., & Xu, C. (2023) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3635155 · Cited by 9 · see citing works
A hybrid approach to real-time multi-target tracking
Scarrica, V. M., Panariello, C., Ferone, A., & Staiano, A. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-09799-4 · Cited by 11 · see citing works
P2FTrack: Multi-Object Tracking with Motion Prior and Feature Posterior
Zhang, H., Wan, J., Zhang, J., Yuan, D., Li, X., & Yang, Y. F. (2024) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3700443 · Cited by 9 · see citing works
LDTrack: Dynamic People Tracking by Service Robots Using Diffusion Models
Fung, A., Benhabib, B., & Nejat, G. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-024-02336-9 · Cited by 9 · see citing works
- 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
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