Deep learning in video multi-object tracking: A survey

Deep Residual Learning for Image Recognition

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Very Deep Convolutional Networks for Large-Scale Image Recognition

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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

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You Only Look Once: Unified, Real-Time Object Detection

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Going deeper with convolutions

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ImageNet Large Scale Visual Recognition Challenge

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Support-Vector Networks

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Histograms of Oriented Gradients for Human Detection

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Self-supervised on-line cumulative learning from video streams

Pernici, F., Bruni, M., & Bimbo, A. D. (2020) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2020.102983 · Cited by 17

A Scalable Reasoning and Learning Approach for Neural-Symbolic Stream Fusion

Le-Phuoc, D., Eiter, T., & Le-Tuan, A. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i6.16633 · Cited by 18

Vision-based Relative Detection and Tracking for Teams of Micro Aerial Vehicles

Ge, R., Lee, M. Y., Radhakrishnan, V., Zhou, Y., Li, G., & Loianno, G. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981115 · Cited by 15

How to track and segment fish without human annotations: a self-supervised deep learning approach

Saleh, A., Sheaves, M. J., Jerry, D. R., & Azghadi, M. R. (2024) · Pattern Analysis and Applications · doi:10.1007/s10044-024-01227-6 · Cited by 15

A 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

GNN-JFL: Graph Neural Network for Video SAR Shadow Tracking With Joint Motion-Appearance Feature Learning

Zhang, W., Zhang, X., Xu, X., Xu, Y., Shao, Z., Shi, J., Wei, S., & Zeng, T. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3383870 · Cited by 14

An efficient visual servo tracker for herd monitoring by UAV

Luo, W., Zhang, G., Shao, Q., Zhao, Y., Wang, D., Zhang, X., Liu, K., Li, X., Liu, J., Wang, P., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-60445-4 · Cited by 14

Interpretable Global-Local Dynamics for the Prediction of Eye Fixations in Autonomous Driving Scenarios

Martínez-Cebrián, J., Fernández‐Torres, M., & Díaz-de-María, F. (2020) · IEEE Access · doi:10.1109/access.2020.3041606 · Cited by 14

A Deep Learning‐Based Power Control and Consensus Performance of Spectrum Sharing in the CR Network

Aslam, M. M., Du, L., Ahmed, Z., Irshad, M. N., & Azeem, H. (2021) · Wireless Communications and Mobile Computing · doi:10.1155/2021/7125482 · Cited by 14

Detecting and Counting Oysters

Sadrfaridpour, B., Aloimonos, Y., Yu, M., Tao, Y., & Webster, D. W. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561268 · Cited by 14

A Deep Learning Bidirectional Temporal Tracking Algorithm for Automated Blood Cell Counting from Non-invasive Capillaroscopy Videos

Huang, L., McKay, G. N., & Durr, N. J. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87237-3_40 · Cited by 14

Pedestrian multiple-object tracking based on FairMOT and circle loss

Che, J., He, Y., & Wu, J. (2023) · Scientific Reports · doi:10.1038/s41598-023-31806-2 · Cited by 14

A spiking convolutional neural network for glioma brain tumor segmentation using a spike-timing-dependent plasticity method

Kasgari, A. B., Sadeghi, S., Zarbakhsh, P., Ghoushchi, S. J., & Ranjbarzadeh, R. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131903 · Cited by 13

CellMAPtracer: A User-Friendly Tracking Tool for Long-Term Migratory and Proliferating Cells Associated with FUCCI Systems

Ghannoum, S., Antos, K., Netto, W. L., Gomes, C. J., Köhn‐Luque, A., & Farhan, H. (2021) · Cells · doi:10.3390/cells10020469 · Cited by 15

Anti-occlusion multi-object surveillance based on improved deep learning approach and multi-feature enhancement for unmanned smart grid safety

Cai, D., Bamisile, O., Zhang, W., Chang, Z., Li, J., Zhang, Z., Wu, J., & Huang, Q. Z. (2023) · Energy Reports · doi:10.1016/j.egyr.2023.01.074 · Cited by 13

COVID-19 infection segmentation using hybrid deep learning and image processing techniques

Antar, S., El-Sattar, H. K. H. A., Abdel-Rahman, M. H., & Ghaleb, F. F. M. (2023) · Scientific Reports · doi:10.1038/s41598-023-49337-1 · Cited by 13

Anomalous object detection by active search with PTZ cameras

López‐Rubio, E., Molina‐Cabello, M. A., Castro, F. M., Luque‐Baena, R. M., Marín-Jiménez, M. J., & Guil, N. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115150 · Cited by 13

Pedestrian Traffic Light Control with Crosswalk FMCW Radar and Group Tracking Algorithm

Nimac, P., Krpič, A., Batagelj, B., & Gams, A. (2022) · Sensors · doi:10.3390/s22051754 · Cited by 12

Editing Motion Graphics Video via Motion Vectorization and Transformation

Zhang, S., Ma, J., Wu, J., Ritchie, D., & Agrawala, M. (2023) · ACM Transactions on Graphics · doi:10.1145/3618316 · Cited by 12

Specific event detection for video surveillance using variational Bayesian inference

Leyva, R., Sánchez, V., Li, C., & Maple, C. R. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128291 · Cited by 12

Deep Learning–Based Scan-to-BIM Automation and Object Scope Expansion Using a Low-Cost 3D Scan Data

Ma, J. W., Jung, J., & Leite, F. L. (2024) · Journal of Computing in Civil Engineering · doi:10.1061/jccee5.cpeng-5751 · Cited by 12

Using computer vision and street-level videos for pedestrian-vehicle tracking and behaviour analysis

Ventura, R., Roussou, S., Ζιακόπουλος, Α., Barabino, B., & Yannis, G. (2025) · Transportation Research Interdisciplinary Perspectives · doi:10.1016/j.trip.2025.101366 · Cited by 12

YOLO11-JDE: Fast and Accurate Multi-Object Tracking with Self-Supervised Re-ID

Erregue, I., Nasrollahi, K., & Escalera, S. (2025) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw65960.2025.00092 · Cited by 12

Automated in vivo Tracking of Cortical Oligodendrocytes

Xu, Y. K. T., Call, C. L., Sulam, J., & Bergles, D. E. (2021) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2021.667595 · Cited by 13

ROW-SLAM: Under-Canopy Cornfield Semantic SLAM

Yuan, J., Hong, J., Sattar, J., & Isler, V. (2022) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra46639.2022.9811745 · Cited by 12

A comparison of deep learning models for end-to-end face-based video retrieval in unconstrained videos

Ciaparrone, G., Chiariglione, L., & Tagliaferri, R. (2022) · Neural Computing and Applications · doi:10.1007/s00521-021-06875-x · Cited by 12

SeeFar: Vehicle Speed Estimation and Flow Analysis from a Moving UAV

Ning, M., Ma, X., Lu, Y., Calderara, S., & Cucchiara, R. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-06433-3_24 · Cited by 12

Multiple Object Tracking Using Re-Identification Model with Attention Module

Ahn, W. J., Ko, K.-S., Lim, M. T., Pae, D.-S., & Kang, T. (2023) · Applied Sciences · doi:10.3390/app13074298 · Cited by 12

Review of Recent Technologies for Tackling COVID-19

Alharbi, A. A., & Rahman, M. A. (2021) · SN Computer Science · doi:10.1007/s42979-021-00841-z · Cited by 14

Multi-view data fusion in multi-object tracking with probability density-based ordered weighted aggregation

Dadgar, A., Baleghi, Y., & Ezoji, M. (2022) · Optik · doi:10.1016/j.ijleo.2022.169279 · Cited by 12

Multi object tracking: a survey

Bouraya, S., & Belangour, A. (2021) · International Conference on Digital Image Processing · doi:10.1117/12.2602901 · Cited by 12

Cascaded Structure Tensor Framework for Robust Identification of Heavily Occluded Baggage Items from Multi-Vendor X-ray ScansPreprint

Hassan, T., Khan, S. H., Akçay, S., Bennamoun, M., & Werghi, N. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.04251 · Cited by 12

Real-Time Multi-Vehicle Multi-Camera Tracking with Graph-Based Tracklet Features

Nguyen, T. T., Nguyen, H. H., Sartipi, M., & Fisichella, M. (2023) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/03611981231170591 · Cited by 13

How do children move and behave on streets? Vision-based movement behavior analysis using children's trajectories in urban surveillance systems

No, W., Choi, J., & Kim, Y. (2023) · Applied Geography · doi:10.1016/j.apgeog.2023.103170 · Cited by 11

Objects do not disappear: Video object detection by single-frame object location anticipation

Liu, X., Nejadasl, F. K., van Gemert, J. C., Booij, O., & Pintea, S. L. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00640 · Cited by 11

Dress Code Monitoring Method in Industrial Scene Based on Improved YOLOv8n and DeepSORT

Zou, J., Song, T., Cao, S., Zhou, B., & Jiang, Q. (2024) · Sensors · doi:10.3390/s24186063 · Cited by 11

Based on improved joint detection and tracking of UAV for multi-target detection of livestock

Shen, P., Wang, F., Luo, W., Zhao, Y., Li, L., Zhang, G., & Zhu, Y. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e38316 · Cited by 11

Advances in vehicle re-identification techniques: A survey

Yi, X. L., Wang, Q., Liu, Q., Rui, Y., & Ran, B. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128745 · Cited by 11

Airline Baggage Appearance Transportability Detection Based on A Novel Dataset and Sequential Hierarchical Sampling CNN Model

Gao, Q., & Liang, P. (2021) · IEEE Access · doi:10.1109/access.2021.3065705 · Cited by 11

ASML: Algorithm-Agnostic Architecture for Scalable Machine Learning

Diamantis, D. E., & Iakovidis, D. K. (2021) · IEEE Access · doi:10.1109/access.2021.3069857 · Cited by 11

Counting, Classifying and Tracking Vehicles Routes at Road Intersections with YOLOv4 and DeepSORT

Gatelli, L., Gosmann, G., Fitarelli, F., Huth, G., Schwertner, A. A., de Azambuja, R., & Brusamarello, V. J. (2021) · International Symposium on Instrumentation Systems, Circuits and Transducers (INSCIT) · doi:10.1109/inscit49950.2021.9557244 · Cited by 11

TagFocus: Towards Fine-Grained Multi-Object Identification in RFID-based Systems with Visual Aids

Yin, J., Yang, Z., Liao, S., Duan, C., Ding, X., & Zhang, L. (2022) · ACM Transactions on Sensor Networks · doi:10.1145/3526193 · Cited by 11

Visual Tracking of mini-UAVs using Modified YOLOv5 and Improved DeepSORT Algorithms

Delleji, T., Fkih, H., Kallel, A., & Chtourou, Z. (2022) · International Conference on Advanced Technologies for Signal and Image Processing · doi:10.1109/atsip55956.2022.9805923 · Cited by 11

Partial Scene Reconstruction for Close Range Photogrammetry Using Deep Learning Pipeline for Region Masking

Eldefrawy, M., King, S. A., & Starek, M. J. (2022) · Remote Sensing · doi:10.3390/rs14133199 · Cited by 11

Instance segmentation and tracking of animals in wildlife videos: SWIFT - segmentation with filtering of tracklets

Schindler, F., & Steinhage, V. (2022) · Ecological Informatics · doi:10.1016/j.ecoinf.2022.101794 · Cited by 11

Real-time siamese multiple object tracker with enhanced proposals

Vaquero, L., Brea, V. M., & Mucientes, M. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.109141 · Cited by 11

Multi-Objective Surrogate-Model-Based Neural Architecture and Physical Design Co-Optimization of Energy Efficient Neural Network Hardware Accelerators

Wöhrle, H., Schneider, F., Schlenke, F., Lebold, D., Álvarez, M. D. L., Kirchner, F., & Karagounis, M. (2022) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2022.3209574 · Cited by 11

The deep arbitrary polynomial chaos neural network or how Deep Artificial Neural Networks could benefit from data-driven homogeneous chaos theory

Oladyshkin, S., Praditia, T., Kröker, I., Mohammadi, F., Nowak, W., & Otte, S. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.06.036 · Cited by 14

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 12

SOTVerse: A User-Defined Task Space of Single Object Tracking

Hu, S., Zhao, X., & Huang, K. (2023) · International Journal of Computer Vision · doi:10.1007/s11263-023-01908-5 · Cited by 11

Semi-automated computer vision-based tracking of multiple industrial entities: a framework and dataset creation approach

Rutinowski, J., Youssef, H., Franke, S., Priyanta, I. F., Polachowski, F., Roidl, M., & Reining, C. (2024) · EURASIP Journal on Image and Video Processing · doi:10.1186/s13640-024-00623-6 · Cited by 11

Edge pre-processing of traffic surveillance video for bandwidth and privacy optimization in smart cities

Skadins, A., Ivanovs, M., Rava, R., & Nesenbergs, K. (2020) · Biennial Baltic Electronics Conference (BEC) · doi:10.1109/bec49624.2020.9276799 · Cited by 11

Human Segmentation and Tracking Survey on Masks for MADS Dataset

Le, V.-H., & Scherer, R. (2021) · Sensors · doi:10.3390/s21248397 · Cited by 11

A new multi‐object tracking pipeline based on computer vision techniques for mussel farms

Zeng, D., Liu, I., Bi, Y., Vennell, R., Briscoe, D. K., Xue, B., & Zhang, M. (2023) · Journal of the Royal Society of New Zealand · doi:10.1080/03036758.2023.2240466 · Cited by 11

Him-Net: A New Neural Network Approach for SAR and Optical Image Template Matching 1

Xu, H., He, M., Rao, Z., & Li, W. (2021) · Proceedings - International Conference on Image Processing · doi:10.1109/icip42928.2021.9506616 · Cited by 10

Hitch Angle Estimation for Trailer Backup System—An Object Detection and Tracking Approach

Bahramgiri, M., Nooshabadi, S., Olutomilayo, K. T., & Fuhrmann, D. R. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3212737 · Cited by 10

Towards the Robustness of Multiple Object Tracking Systems

Xie, X., Duan, Y., Chen, S., & Xuan, J. (2022) · Proceedings/Proceedings - International Symposium on Software Reliability Engineering · doi:10.1109/issre55969.2022.00046 · Cited by 10

Decision Support Systems in Architecture—A Future Perspective

Schubert, G., Bratoev, I., & Petzold, F. (2023) · Buildings · doi:10.3390/buildings13081952 · Cited by 12

A Robust Framework for Epidemic Analysis, Prediction and Detection of COVID-19

Hassan, F., Albahli, S., Javed, A., & Irtaza, A. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.805086 · Cited by 10

Periodicity counting in videos with unsupervised learning of cyclic embeddings

Jacquelin, N., Vuillemot, R., & Duffner, S. (2022) · Pattern Recognition Letters · doi:10.1016/j.patrec.2022.07.013 · Cited by 15

Vehicle Analysis System Based on DeepSORT and YOLOv5

Guo, F., & Xu, Y. (2022) · 2022 3rd International Conference on Computer Vision, Image and Deep Learning & International Conference on Computer Engineering and Applications (CVIDL & ICCEA) · doi:10.1109/cvidliccea56201.2022.9824363 · Cited by 10

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

Multiple Pedestrian Tracking Framework using Deep Learning-based Multiscale Image Analysis for Stationary-camera Video Surveillance

Barbu, T. (2022) · IEEE International Smart Cities Conference (ISC2) · doi:10.1109/isc255366.2022.9922217 · Cited by 10

A novel Siamese network object tracking algorithm based on tensor space mapping and memory-learning mechanism

Wu, Y., Zhang, B., Lu, X., Gu, Y., Wang, Y., Liu, X., Ren, Y., & Li, J. (2023) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2022.103742 · Cited by 10

Combining Spiking Neural Networks with Artificial Neural Networks for Enhanced Image Classification

Muramatsu, N., Yu, H.-T., & Satoh, T. (2023) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2021edp7237 · Cited by 11

OPTIMIZING PEDESTRIAN TRACKING FOR ROBUST PERCEPTION WITH YOLOv8 AND DEEPSORT

Zidani, G., Djarah, D., Benmakhlouf, A., & Khettache, L. (2024) · Applied Computer Science · doi:10.35784/acs-2024-05 · Cited by 10

Enhancing Multi-Camera Gymnast Tracking Through Domain Knowledge Integration

Yang, F., Odashima, S., Masui, S., Kusajima, I., Yamao, S., & Jiang, S. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3447670 · Cited by 10

Video-Based Traffic Flow Analysis for Turning Volume Estimation at Signalized Intersections

Bui, K. N., Yi, H., Jung, H., & Cho, J.-H. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-42058-1_13 · Cited by 10

Multi-objective Magnitude-Based Pruning for Latency-Aware Deep Neural Network Compression

Hong, W., Yang, P., Wang, Y., & Tang, K. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58112-1_32 · Cited by 10

DeepDish

Danish, M., Brazauskas, J., Bricheno, R., Lewis, I., & Mortier, R. (2020) · doi:10.1145/3378679.3394535 · Cited by 10

Smart Health Monitoring for Seizure Detection using Mobile Edge Computing

Ali, Z. S., Subramanian, N., & Erbad, A. (2020) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc48107.2020.9148418 · Cited by 10

A review of UAV Visual Detection and Tracking MethodsPreprint

Zitar, R. A., Al-Betar, M. A., Ryalat, M., & Kassaymeh, S. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2306.05089 · Cited by 10

Unmanned Aerial Vehicle Visual Detection and Tracking using Deep Neural Networks: A Performance Benchmark

Isaac-Medina, B. K. S., Poyser, M., Organisciak, D., Willcocks, C. G., Breckon, T. P., & Shum, H. P. H. (2021) · 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) · doi:10.1109/iccvw54120.2021.00142 · Cited by 9

SpikeMOT: Event-Based Multi-Object Tracking With Sparse Motion Features

Wang, S., Wang, Z., Li, C., Qi, X., & So, H. K. (2024) · IEEE Access · doi:10.1109/access.2024.3523411 · Cited by 9

Facing challenges: A survey of object tracking

Zhang, W., Xinqiang, L., Liu, X., Lu, S., & Tang, H. (2025) · Digital Signal Processing · doi:10.1016/j.dsp.2025.105082 · Cited by 9

BlockNet: A Deep Neural Network for Block-Based Motion Estimation Using Representative Matching

Lee, J., Kong, K., Bae, G., & Song, W. (2020) · Symmetry · doi:10.3390/sym12050840 · Cited by 9

SPT: Single Pedestrian Tracking Framework with Re-Identification-Based Learning Using the Siamese Model

Manzoor, S., An, Y.-C., In, G.-G., Zhang, Y., Kim, S., & Kuc, T. (2023) · Sensors · doi:10.3390/s23104906 · Cited by 9

Deep learning based object tracking in walking droplet and granular intruder experiments

Kara, E., Zhang, G., Williams, J. J., Ferrandez-Quinto, G., Rhoden, L. J., Kim, M., Kutz, J. N., & Rahman, A. (2023) · Journal of Real-Time Image Processing · doi:10.1007/s11554-023-01341-4 · Cited by 9

Camera-based occupant behavior trajectory capture and on-site assessment of spatio-temporal transmission risk of airborne virus in a university office building

Zhong, Z., Pan, Z., Tang, H., Wang, J., Liu, F., & Wang, J. (2023) · Building and Environment · doi:10.1016/j.buildenv.2023.110670 · Cited by 11

Tracking of moving human in different overlapping cameras using Kalman filter optimized

Yousefi, S. M. M., Mohseni, S. S., Dehbovid, H., & Ghaderi, R. (2023) · EURASIP Journal on Advances in Signal Processing · doi:10.1186/s13634-023-01078-z · Cited by 9

An efficient center-based method for real-time pig posture recognition and tracking

Mattina, M., Benzinou, A., Nasreddine, K., & Richard, F. (2024) · Applied Intelligence · doi:10.1007/s10489-024-05439-5 · Cited by 9

3D zebrafish tracking with topology association

Xu, Y., Jin, Y., Zhang, Y., Zhu, Q., He, Y., & Sheng, H. (2022) · IET Image Processing · doi:10.1049/ipr2.12694 · Cited by 9

Improving Multiple Pedestrian Tracking in Crowded Scenes with Hierarchical Association

Xiao, C., & Luo, Z. (2023) · Entropy · doi:10.3390/e25020380 · Cited by 9

Fast Segmentation-Based Object Tracking Model for Autonomous Vehicles

Dong, X., Niu, J., Cui, J., Fu, Z., & Ouyang, Z. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60239-0_18 · Cited by 9

Flow-network adaptation and behavior in slime molds

Dussutour, A., & Arson, C. F. (2024) · Fungal ecology · doi:10.1016/j.funeco.2023.101325 · Cited by 8

ArbiTrack: A Novel Multi-Object Tracking Framework for a Moving AAV to Detect and Track Arbitrarily Oriented Targets

Chen, Y., Wang, J., Zhou, Q., & Hu, H. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3543018 · Cited by 8

DeepAF: Transformer-Based Deep Data Association and Track Filtering Network for Multi-Target Tracking in Clutter

Cui, Y., Xu, P., Sun, W., Zhang, S., & Li, J. (2025) · Aerospace · doi:10.3390/aerospace12030194 · Cited by 8

Multi-cue multi-hypothesis tracking with re-identification for multi-object tracking

Guo, W. Q., Jin, Y., Shan, B., Ding, X., & Wang, M. (2022) · Multimedia Systems · doi:10.1007/s00530-022-00895-w · Cited by 8

Road and Railway Smart Mobility: A High-Definition Ground Truth Hybrid Dataset

Khemmar, R., Mauri, A., Dulompont, C., Gajula, J., Vauchey, V., Haddad, M., & Boutteau, R. (2022) · Sensors · doi:10.3390/s22103922 · Cited by 8

Robust appearance modeling for object detection and tracking: a survey of deep learning approaches

Mumuni, A., & Mumuni, F. (2022) · Progress in Artificial Intelligence · doi:10.1007/s13748-022-00290-6 · Cited by 8

3D Multi-Object Tracking Based on Radar-Camera Fusion

Lin, Z., & Hu, J. (2022) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc55140.2022.9921931 · Cited by 8

Acquiring Weak Annotations for Tumor Localization in Temporal and Volumetric Data

Chou, Y.-C., Li, B., Fan, D.-P., Yuille, A., & Zhou, Z. (2024) · Machine Intelligence Research · doi:10.1007/s11633-023-1380-5 · Cited by 8

An Efficient Multi-Object Tracking Guided by Spatial Clustering on Vision Sensors

Chen, Y. H., Li, Z., Wu, L., & Chen, P. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3391510 · Cited by 8

Collaborative Multi-Object Tracking as an Edge Service using Transfer Learning

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