Online multi-object tracking with pedestrian re-identification and occlusion processing

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works

Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741 · see citing works

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 · see citing works

Simple online and realtime tracking

Bewley, A., Ge, Z., Ott, L., Ramos, F., & Upcroft, B. (2016) · 2016 IEEE International Conference on Image Processing (ICIP) · doi:10.1109/icip.2016.7533003 · Cited by 4,433 · see citing works

In Defense of the Triplet Loss for Person Re-IdentificationPreprint

Hermans, A., Beyer, L., & Leibe, B. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.07737 · Cited by 3,271 · see citing works

Performance Measures and a Data Set for Multi-target, Multi-camera Tracking

Ristani, E., Solera, F., Zou, R., Cucchiara, R., & Tomasi, C. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-48881-3_2 · Cited by 3,104 · see citing works

MOTChallenge 2015: Towards a Benchmark for Multi-Target TrackingPreprint

Leal-Taixé, L., Milan, A., Reid, I. D., Roth, S., & Schindler, K. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1504.01942 · Cited by 923 · see citing works

High-Speed tracking-by-detection without using image information

Bochinski, E., Eiselein, V., & Sikora, T. (2017) · 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2017.8078516 · Cited by 707 · see citing works

Learning to Track: Online Multi-object Tracking by Decision Making

Yu, X., Alahi, A., & Savarese, S. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.534 · Cited by 684 · see citing works

A Potential Field-Based Model Predictive Path-Planning Controller for Autonomous Road Vehicles

Rasekhipour, Y., Khajepour, A., Chen, S.-K., & Litkouhi, B. B. (2016) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2016.2604240 · Cited by 658 · see citing works

Tracking the Untrackable: Learning to Track Multiple Cues with Long-Term Dependencies

Sadeghian, A., Alahi, A., & Savarese, S. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.41 · Cited by 567 · see citing works

Continuous Energy Minimization for Multitarget Tracking

Milan, A., Roth, S., & Schindler, K. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.103 · Cited by 556 · see citing works

Robust tracking-by-detection using a detector confidence particle filter

Breitenstein, M. D., Reichlin, F., Leibe, B., Koller-Meier, E., & Gool, L. V. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459278 · Cited by 555 · see citing works

Online Multi-Target Tracking Using Recurrent Neural Networks

Milan, A., Rezatofighi, S. H., Dick, A., Reid, I. D., & Schindler, K. (2017) · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · doi:10.1609/aaai.v31i1.11194 · Cited by 514 · see citing works

POI: Multiple Object Tracking with High Performance Detection and Appearance Feature

Yu, F., Li, W., Li, Q., Liu, Y., Shi, X., & Yan, J. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-48881-3_3 · Cited by 482 · see citing works

Online Multi-object Tracking Using CNN-Based Single Object Tracker with Spatial-Temporal Attention Mechanism

Chu, Q., Ouyang, W., Li, H., Wang, X., Liu, B., & Yu, N. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.518 · Cited by 406 · see citing works

Online Multi-Object Tracking with Dual Matching Attention Networks

Zhu, J., Yang, H., Liu, N., Kim, M., Zhang, W., & Yang, M.-H. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01228-1_23 · Cited by 377 · see citing works

A Unified Framework for Multi-target Tracking and Collective Activity Recognition

Choi, W., & Savarese, S. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33765-9_16 · Cited by 359 · see citing works

Recurrent Autoregressive Networks for Online Multi-object Tracking

Fang, K., Yu, X., Li, X., & Savarese, S. (2018) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2018.00057 · Cited by 278 · see citing works

Spatial-Temporal Relation Networks for Multi-Object Tracking

Xu, J., Cao, Y., Zhang, Z., & Hu, H. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00409 · Cited by 275 · see citing works

Multi-target Tracking by Lagrangian Relaxation to Min-cost Network Flow

Butt, A. A., & Collins, R. T. (2013) · 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/cvpr.2013.241 · Cited by 265 · see citing works

Online Multi-target Tracking with Strong and Weak Detections

Sánchez-Matilla, R., Poiesi, F., & Cavallaro, A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-48881-3_7 · Cited by 242 · see citing works

Multi-target tracking using CNN-based features: CNNMTT

Mahmoudi, N., Ahadi, S. M., & Rahmati, M. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6467-6 · Cited by 163 · see citing works

Preserving Structure in Model-Free Tracking

Zhang, L., & van der Maaten, L. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.221 · Cited by 150 · see citing works

Improving multi-target tracking via social grouping

Qin, Z., & Shelton, C. R. (2012) · 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/cvpr.2012.6247899 · Cited by 132 · see citing works

Using Convolutional 3D Neural Networks for User-independent continuous gesture recognition

Camgöz, N. C., Hadfield, S., Koller, O., & Bowden, R. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7899606 · Cited by 92 · see citing works

Online Multi-Object Tracking with Historical Appearance Matching and Scene Adaptive Detection Filtering

Yoon, Y.-C., Boragule, A., Song, Y.-M., Yoon, K., & Jeon, M. (2018) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2018.8639078 · Cited by 83 · see citing works

(MP)2T: Multiple People Multiple Parts Tracker

Izadinia, H., Saleemi, I., Li, W., & Shah, M. A. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33783-3_8 · Cited by 78 · see citing works

Data Association for Multi-Object Tracking via Deep Neural Networks

Yoon, K., Kim, D. Y., Yoon, Y.-C., & Jeon, M. (2019) · Sensors · doi:10.3390/s19030559 · Cited by 68 · see citing works

Real-time multi-scale tracking based on compressive sensing

Yun-xia, W., Jia, N., & Jiping, S. (2014) · The Visual Computer · doi:10.1007/s00371-014-0942-5 · Cited by 36 · see citing works

Online Multi-Object Tracking Using Selective Deep Appearance Matching

Yoon, Y.-C., Song, Y.-M., Yoon, K., & Jeon, M. (2018) · IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia) · doi:10.1109/icce-asia.2018.8552105 · Cited by 27 · see citing works

Object tracking based on Huber loss function

Wang, Y., Hu, S., & Wu, S. (2018) · The Visual Computer · doi:10.1007/s00371-018-1563-1 · Cited by 18 · see citing works

Face detection and tracking using hybrid margin-based ROI techniques

Rehman, B., Ong, W.-H., Tan, A. C. H., & Ngo, T. D. (2019) · The Visual Computer · doi:10.1007/s00371-019-01649-y · Cited by 18 · see citing works

Multi-Object tracking using multi-channel part appearance representation

Anh, N. T. L., Khan, F. M., Negin, F., & Brémond, F. (2017) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2017.8078552 · Cited by 16 · see citing works

Joint cost minimization for multi-object tracking

Boragule, A., & Jeon, M. (2017) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2017.8078481 · Cited by 15 · see citing works

Coupled-layer based visual tracking via adaptive kernelized correlation filters

Zhang, H., & Liu, G. (2016) · The Visual Computer · doi:10.1007/s00371-016-1310-4 · Cited by 11 · see citing works

Moving object tracking based on multi-independent features distribution fields with comprehensive spatial feature similarity

Li, Z., Yu, X., Li, P., & Hashem, M. (2014) · The Visual Computer · doi:10.1007/s00371-014-1044-0 · Cited by 9 · see citing works

How do deep convolutional features affect tracking performance: an experimental study

Guan, H., & Cheng, B. (2017) · The Visual Computer · doi:10.1007/s00371-017-1445-y · Cited by 9 · see citing works

Revisiting correlation-based filters for low-resolution and long-term visual tracking

Fazl-Ersi, E., & Nooghabi, M. K. (2018) · The Visual Computer · doi:10.1007/s00371-018-1510-1 · Cited by 8 · see citing works

A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets

Bayoudh, K., Knani, R., Hamdaoui, F., & Mtibaa, A. (2022) · The Visual Computer · doi:10.1007/s00371-021-02166-7 · Cited by 456 · see citing works

Sort and Deep-SORT Based Multi-Object Tracking for Mobile Robotics: Evaluation with New Data Association Metrics

Pereira, R., Carvalho, G., Garrote, L., & Nunes, U. (2022) · Applied Sciences · doi:10.3390/app12031319 · Cited by 116 · see citing works

TPRNet: camouflaged object detection via transformer-induced progressive refinement network

Zhang, Q., Ge, Y., Zhang, C., & Bi, H. (2022) · The Visual Computer · doi:10.1007/s00371-022-02611-1 · Cited by 96 · see citing works

Pedestrian Detection and Tracking System Based on Deep-SORT, YOLOv5, and New Data Association Metrics

Razzok, M., Badri, A., Mourabit, I. E., Ruichek, Y., & Sahel, A. (2023) · Information · doi:10.3390/info14040218 · Cited by 46 · see citing works

A review of object tracking methods: From general field to autonomous vehicles

Cao, J., Zhang, H., Jin, L., Lv, J., Hou, G., & Zhang, C. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127635 · Cited by 32 · see citing works

Multiple Pedestrian Tracking Under Occlusion: A Survey and Outlook

Sun, Z., Wei, G., Fu, W. E., Ye, M., Jiang, K., Liang, C., Zhu, T., He, T., & Mukherjee, M. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3481425 · Cited by 35 · see citing works

Online multi-object tracking with δ-GLMB filter based on occlusion and identity switch handling

Abbaspour, M., & Masnadi‐Shirazi, M. A. (2022) · Image and Vision Computing · doi:10.1016/j.imavis.2022.104553 · Cited by 27 · see citing works

SegDQ: Segmentation assisted multi-object tracking with dynamic query-based transformers

Liu, Y., Bai, T., Tian, Y., Wang, Y., Wang, J., Wang, X., & Wang, F. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.01.073 · Cited by 20 · see citing works

Center-point-pair detection and context-aware re-identification for end-to-end multi-object tracking

Zhang, X., Ling, Y., Yang, Y., Chu, C., & Zhou, Z. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.11.094 · Cited by 19 · see citing works

MotionTrack: rethinking the motion cue for multiple object tracking in USV videos

Liang, Z., Xiao, G., Hu, J., Wang, J., & Ding, C. (2022) · The Visual Computer · doi:10.1007/s00371-023-02983-y · Cited by 18 · see citing works

Camouflaged object detection via cross-level refinement and interaction network

Ge, Y., Ren, J., Zhang, Q., He, M., Bi, H., & Zhang, C. (2024) · Image and Vision Computing · doi:10.1016/j.imavis.2024.104973 · Cited by 17 · see citing works

A paced multi-stage block-wise approach for object detection in thermal images

Kera, S. B., Tadepalli, A., & Ranjani, J. J. (2022) · The Visual Computer · doi:10.1007/s00371-022-02445-x · Cited by 15 · see citing works

Pedestrian multi-object tracking based on YOLOv7 and BoT-SORT

Li, T., Li, Z., Mu, Y., & Su, J. (2023) · International Conference on Computer Vision and Pattern Analysis (ICCPA) · doi:10.1117/12.2684256 · Cited by 11 · see citing works

A lightweight scheme of deep appearance extraction for robust online multi-object tracking

Li, Y., Liu, Y., Zhou, C., Xu, D., & Tao, W. (2023) · The Visual Computer · doi:10.1007/s00371-023-02901-2 · Cited by 9 · see citing works

Ownership of abandoned object detection by integrating carried object recognition and context sensing

Russel, N. S., & Selvaraj, A. (2023) · The Visual Computer · doi:10.1007/s00371-023-03089-1 · Cited by 9 · see citing works

JDECMC: Improving JDE based multi-object tracking with Camera Motion Compensation

Sinishaw, M. L., & Liu, S. (2024) · Displays · doi:10.1016/j.displa.2024.102682 · Cited by 8 · see citing works

ODSPC: deep learning-based 3D object detection using semantic point cloud

Song, S., Huang, T., Zhu, Q., & Hu, H. (2023) · The Visual Computer · doi:10.1007/s00371-023-02820-2 · Cited by 6 · see citing works

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 · see citing works

Collision-Line Counting Method Using DeepSORT to Count Pedestrian Flow Density and Hungary Algorithm

Pei, Y., Liu, H., & Bei, Q. (2021) · IEEE International Conference on Civil Aviation Safety and Information Technology (ICCASIT) · doi:10.1109/iccasit53235.2021.9633356 · Cited by 5 · see citing works

A Mixed Method for Feature Extraction Based on Resonance Filtering

Zhang, X., Lu, W., Ding, Y., Song, Y., & Xia, J. (2022) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2023.027219 · Cited by 5 · see citing works

FPGA accelerated DeepSORT object tracking

Szántó, P., Kiss, T., & Sipos, K. J. (2023) · International Carpathian Control Conference · doi:10.1109/iccc57093.2023.10178935 · Cited by 4 · see citing works

An embedded platform approach to privacy-centric person re-identification

Pym, N., & Freitas, A. D. (2021) · International Conference on Information Fusion (FUSION) · doi:10.23919/fusion49465.2021.9626896 · Cited by 4 · see citing works

Energy-Efficient Relay Tracking and Predicting Movement Patterns with Multiple Mobile Camera Sensors

Hussein, Z., & Banimelhem, O. (2023) · Journal of Sensor and Actuator Networks · doi:10.3390/jsan12020035 · Cited by 4 · see citing works

Real-Time In/Out Crowd Counting System Utilizing YOLOv8 with DeepSORT

Alano, J. M. L., Agtunong, J. R. G., & Latina, M. A. E. (2024) · International Conference on Electrical, Telecommunication and Computer Engineering (ELTICOM) · doi:10.1109/elticom64085.2024.10864842 · Cited by 2 · see citing works

Tracking plant growth using image sequence analysis

Szoke, Y., & Shani, G. (2025) · Agriculture Communications · doi:10.1016/j.agrcom.2025.100110 · Cited by 2 · see citing works

Head Point Positioning and Spatial-Channel Self-Attention Network for Multi-Object Tracking

Zhang, X., Gao, S., Yang, Y., Chu, C., & Zhou, Z. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956665 · Cited by 2 · see citing works

A Multi-Object Tracking framework with feature backtracking under severe occlusion

Ma, J., Zheng, Y., Lyu, Y., Jia, C., & Su, Y. (2026) · Ocean Engineering · doi:10.1016/j.oceaneng.2026.124175 · Cited by 2 · see citing works

INCREMENTAL MAPPING OF PEOPLE'S DISTRIBUTION WITH A MOBILE OMNIDIRECTIONAL CAMERA

Muraoka, T., KUBOTA, S., & Yasumuro, Y. (2023) · Japanese Journal of JSCE · doi:10.2208/jscejj.22-22017 · Cited by 1 · see citing works

Real-Time Pedestrian Tracking System Based on Depth Camera for Quadruped Robot

Zhang, Y., Ren, X., & Zheng, D. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-6886-2_40

Reinforce Model Tracklet for Multi-Object Tracking

Ouyang, J., Wang, S., Yang, Z., Wu, Y., Shen, J., & Sheng, H. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-50075-6_7

Super-Resolution Video Target Re-Recognition Based on Joint Training

Chen, J., Chen, Z., Liu, Z., & Tu, P. (2024) · International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) · doi:10.1109/mlise62164.2024.10674371

Passive Perception and Path Tracking of Tourists in Mountain Scenic Spots Through Face to Body Two Stepwise Method

Yang, F., Zhu, C., Shi, K., Li, J., Shen, Q., & Zhang, X. (2024) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi13120423

Multi-pedestrian tracking based on iterative filtering and observation discrimination

Yang, C., Xu, A., Li, Y., & Wu, X. (2025) · Signal Image and Video Processing · doi:10.1007/s11760-025-04504-x

Multispectral Images-Based Enhanced Pedestrian Detection System Using Deep Learning

Rahul, M., Shukla, H., Virmani, A., Singh, A., Srivastava, P., Mishra, D. K., & Yadav, V. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-02790-0_20

Re-identification assistance and multi-stage association for pedestrian multi-object tracking

Li, Y., Li, Z., Wu, L., Liu, H., Min, J., & Wang, X. (2025) · Scientific Reports · doi:10.1038/s41598-025-07276-z · Cited by 5 · see citing works

Improving Online Multi-Person Tracking Occlusion: Scale Loss for Deep ReID Feature Learning

Yang, H. (. (2018) · Research Repository (Delft University of Technology)

Occlusion-Aware Detection and Re-ID Calibrated Network for Multi-Object TrackingPreprint

Su, Y., Sun, R., Shu, X. Y., Zhang, Y., & Wu, Q. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2308.15795 · Cited by 4 · see citing works

Multi-Object Tracking Based on Re-Identification Enhancement and Associated Correction

Li, J., & Piao, Y. (2023) · Applied Sciences · doi:10.3390/app13179528 · Cited by 7 · see citing works

OccluSort: pedestrian multi-object tracking in occlusion scenarios based on dynamic occlusion-aware re-identification

Wang, S., Shi, X., Li, Y., Sun, K., & Liu, M. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/ae2351