Multiple Person Tracking based on Spatial-temporal Information by Global Graph Clustering

• Yu-ting Su,
• Xiao-rong Zhu,
• Wei-Zhi Nie,

Vol. 9, No. 6, June 29, 2015
10.3837/tiis.2015.06.014, Download Paper (Free):

• Multiple person tracking
• object tracking
• multiple cameras tracking
• graph clustering

Abstract

Since the variations of illumination, the irregular changes of human shapes, and the partial occlusions, multiple person tracking is a challenging work in computer vision. In this paper, we propose a graph clustering method based on spatio-temporal information of moving objects for multiple person tracking. First, the part-based model is utilized to localize individual foreground regions in each frame. Then, we heuristically leverage the spatio-temporal constraints to generate a set of reliable tracklets. Finally, the graph shift method is applied to handle tracklet association problem and consequently generate the completed trajectory for individual object. The extensive comparison experiments demonstrate the superiority of the proposed method.

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