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| Adaptive Deep Multi-object Tracking Algorithm Fusing Crowd Density |
| LIU Jinwen1,2,3, REN Weihong4, TIAN Jiandong1,2 |
1. Robotics Laboratory, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110169;
2. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169;
3. School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049;
4. School of Mechanical Engineering and Automation, Harbin Institute of Technology(Shenzhen), Shenzhen 518055 |
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Abstract Multi-object tracking technology cannot well solve the problem of multi-object tracking in the scenarios with objects severely occluded, and therefore an adaptive deep multi-object tracking algorithm fusing crowd density is proposed. Firstly, the crowd density maps and object detection results are fused, and the location and the count information of crowd density maps are utilized to correct the detector results to eliminate missing and false detections. Then, adaptive triplet loss is employed to improve the loss function of the re-identification model and thus the discrimination of the algorithm for the re-identification feature is enhanced. Finally, final tracking results are obtained using the appearance and motion information for objects association. It is verified through the experiments that the proposed algorithm effectively solves the problem of multi-object tracking in severely occluded scenes.
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Received: 11 January 2021
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| Fund:National Natural Science Foundation of China(No.U2013210, 61821005) |
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Corresponding Authors:
TIAN Jiandong, Ph.D., professor. His research interests include pattern recognition and robot vision.
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| About author:: LIU Jinwen, master student. His research interests include image processing and object tracking.REN Weihong, Ph.D., assistant profe-ssor. His research interests include image pro-cessing and pattern recognition. |
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2021, Vol. 34 
