Jaehyun So, Hernsoo Hahn, Youngjun Han
(Dept. of Electronic Engineering, Soongsil University, Seoul 156-743, Korea)
Abstract：This paper proposes a pedestrian tracking approach using bounding box based on probability densities. It is generally a difficult task to track features like corner points in outdoor images due to complex environment. To solve this problem, the feature points are projected along X and Y direction separately, and a histogram is constructed for each projection, with horizontal axis as positions and vertical axis as the number of feature points that lie on each position. Finally, the vertical axis is normalized for expression as probability. After histogram is constructed, the probability of each feature point is checked with a threshold. A feature point will be ignored if its probability is lower than a threshold, while the remaining feature points are grouped, based on which a bounding box is made. Kanade-Lucas Tomasi(KLT) algorithm is adopted as the tracking algorithm because it is able to track local features in images robustly. The efficiency of the tracking results using this method is verified in real environment test.
Key words：pedestrian tracking; probability density; bounding box; Kanade-Lucas Tomasi(KLT) feature tracker
CLD number： TP391.41 Document code： A
Article ID： 1674-8042(2012)04-0333-03 doi： 10.3969/j.issn.1674-8042.2012.04.007
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