DocumentCode :
3006236
Title :
Multi-object tracking through occlusions by local tracklets filtering and global tracklets association with detection responses
Author :
Junliang Xing ; Haizhou Ai ; Shihong Lao
Author_Institution :
Comput. Sci. & Technol. Dept., Tsinghua Univ., Beijing, China
fYear :
2009
fDate :
20-25 June 2009
Firstpage :
1200
Lastpage :
1207
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.
Keywords :
object detection; particle filtering (numerical methods); surveillance; Hungarian algorithm; dense visual surveillances scenario; detection response; full object occlusion; global tracklets association; local tracklets filtering; observer selection; online detection; partial object occlusion; particle filter; temporal sliding window; two-stage multiobject tracking; Cameras; Cost function; Detectors; Filtering; Humans; Object detection; Particle filters; Particle tracking; Robustness; Surveillance;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on
Conference_Location :
Miami, FL
ISSN :
1063-6919
Print_ISBN :
978-1-4244-3992-8
Type :
conf
DOI :
10.1109/CVPR.2009.5206745
Filename :
5206745
Link To Document :
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