ML Anthology

Multi-Object Tracking Through Occlusions by Local Tracklets Filtering and Global Tracklets Association with Detection Responses

Junliang Xing, Haizhou Ai, Shihong Lao
CVPR 2009 pp. 1200-1207
doi:10.1109/CVPR.2009.5206745 /cvpr/2009/xing2009cvpr-multi/

Abstract

This paper presents an online detection-based two-stage multi-object tracking method in dense visual surveillances scenarios with a single camera. In the local stage, a particle filter with observer selection that could deal with partial object occlusion is used to generate a set of reliable tracklets. In the global stage, the detection responses are collected from a temporal sliding window to deal with ambiguity caused by full object occlusion to generate a set of potential tracklets. The reliable tracklets generated in the local stage and the potential tracklets generated within the temporal sliding window are associated by Hungarian algorithm on a modified pairwise tracklets association cost matrix to get the global optimal association. This method is applied to the pedestrian class and evaluated on two challenging datasets. The experimental results prove the effectiveness of our method.

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Cite

Text

Xing et al. "Multi-Object Tracking Through Occlusions by Local Tracklets Filtering and Global Tracklets Association with Detection Responses." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2009. doi:10.1109/CVPR.2009.5206745

Markdown

[Xing et al. "Multi-Object Tracking Through Occlusions by Local Tracklets Filtering and Global Tracklets Association with Detection Responses." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2009.](https://mlanthology.org/cvpr/2009/xing2009cvpr-multi/) doi:10.1109/CVPR.2009.5206745

BibTeX

@inproceedings{xing2009cvpr-multi,
  title     = {{Multi-Object Tracking Through Occlusions by Local Tracklets Filtering and Global Tracklets Association with Detection Responses}},
  author    = {Xing, Junliang and Ai, Haizhou and Lao, Shihong},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2009},
  pages     = {1200-1207},
  doi       = {10.1109/CVPR.2009.5206745},
  url       = {https://mlanthology.org/cvpr/2009/xing2009cvpr-multi/}
}