自动化学报2017，Vol.43Issue(1)：152-160,9.DOI:10.16383/j.aas.2017.c150519
基于广义关联聚类图的分层关联多目标跟踪
Multi-object Tracking Using Hierarchical Data Association Based on Generalized Correlation Clustering Graphs
摘要
Abstract
Tracking by detection based on data association of detections is a main research direction in the field of multi-object tracking. The majority of current methods, such as bipartite matching, solve the data association problem between adjacent frames. We propose an approach to solve data association for one object whose all detections are in a sliding temporal window at a time. The task of multi-object tracking can be considered as a graph partitioning problem that takes the form of a generalized correlation clustering problem (GCCP). The multi-object tracking problem can be divided into two phases using hierarchical data association. Firstly, adaptive length tracklets can be obtained from all detections in a sliding temporal window by using the GCCP. The length of tracklets is not restricted to the window width. Secondly, we treat tracklets as detections, using the similar method described in first phase to acquire trajectories. Experiments show that the proposed method can handle occlusion and ID-switch effectively, which makes significant improvement in multi-ob ject tracking on the public datasets. Our multiple ob ject tracking accuracy (MOTA) is higher than that of the-state-of-the-art.关键词
多目标跟踪/广义关联聚类图/分层数据关联/检测跟踪/遮挡处理Key words
Multi-object tracking/generalized correlation clustering graphs (GCCP)/hierarchical data association/tracking-by-detection/occlusion handing引用本文复制引用
齐美彬,岳周龙,疏坤,蒋建国..基于广义关联聚类图的分层关联多目标跟踪[J].自动化学报,2017,43(1):152-160,9.基金项目
国家自然科学基金(61371155),安徽省科技攻关项目(1301b042023)资助Supported by National Natural Science Foundation of China (61371155), Science and Technology Brainstorm Project of An-hui Province (1301b042023) （61371155）
