Rethinking Bipartite Graph Matching in Realtime Multi-object Tracking | |
Zhuojun Zou1,2![]() ![]() | |
2022-08 | |
会议名称 | 2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML) |
会议日期 | 25-27 March 2022 |
会议地点 | Hangzhou, China |
摘要 | Data association is a crucial part for tracking-by-detection framework. Although many works about constructing the matching cost between trajectories and detections have been proposed in the community, few researchers pay attention to how to improve the efficiency of bipartite graph matching in realtime multi-object tracking. In this paper, we start with the optimal solution of integer linear programming, explore the best application of bipartite graph matching in tracking task and evaluate the rationality of cost matrix simultaneously. Frist, we analyze the defects of bipartite graph matching process in some multi-object tracking methods, and establish a criteria of similarity measure between trajectories and detections. Then we design two weight matrices for multi-object tracking by applying our criteria. Besides, a novel tracking process is proposed to handle visual-information-free scenario. Our method improves the accuracy of the graph-matching-based approach at very fast running speed (3000+ FPS). Comprehensive experiments performed on MOT benchmarks demonstrate that the proposed approach achieves the state-of-the-art performance in methods without visual information. Moreover, the efficient matching process can also be assembled on approaches with appearance information to replace cascade matching. |
七大方向——子方向分类 | 目标检测、跟踪与识别 |
国重实验室规划方向分类 | 视觉信息处理 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/52272 |
专题 | 国家专用集成电路设计工程技术研究中心_实感计算 |
通讯作者 | Jie Hao |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences 3.Guangdong Institute of Artificial Intelligence and Advanced Computing |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Zhuojun Zou,Jie Hao,Lin Shu. Rethinking Bipartite Graph Matching in Realtime Multi-object Tracking[C],2022. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Rethinking_Bipartite(260KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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