• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

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

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

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.


Statistics

Show / Hide Statistics

Statistics (Cumulative Counts from December 1st, 2015)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article

[IEEE Style]
Y. Su, X. Zhu and W. Nie, "Multiple Person Tracking based on Spatial-temporal Information by Global Graph Clustering," KSII Transactions on Internet and Information Systems, vol. 9, no. 6, pp. 2217-2229, 2015. DOI: 10.3837/tiis.2015.06.014.

[ACM Style]
Yu-ting Su, Xiao-rong Zhu, and Wei-Zhi Nie. 2015. Multiple Person Tracking based on Spatial-temporal Information by Global Graph Clustering. KSII Transactions on Internet and Information Systems, 9, 6, (2015), 2217-2229. DOI: 10.3837/tiis.2015.06.014.