This paper proposes a vision-based
pedestrian detection in crowded situations based on a single camera. The main
idea behind our work is to fuse multiple cues so that the major challenges,
such as occlusion and complex background facing in the topic of crowd detection
can be successfully overcome. Based on the assumption that human heads are
visible, circle Hough transform (CHT) is applied to detect all circular regions
and each of which is considered as the head candidate of a pedestrian. After
that, the false candidates resulting from complex background are firstly
removed by using template matching algorithm. Two proposed cues called head
foreground contrast (HFC) and block color relation (BCR) are incorporated for
further verification. The rectangular region of every detected human is
determined by the geometric relationships as well as foreground mask extracted
through background subtraction process. Three videos are used to validate the
proposed approach and the experimental results show that the proposed method effectively
lowers the false positives at the expense of little detection rate.
Cite this paper
S. Huang, F. Chang and C. Lu, "Combining Multiple Cues for Pedestrian Detection in Crowded Situations," Journal of Signal and Information Processing, Vol. 4 No. 3, 2013, pp. 62-65. doi: 10.4236/jsip.2013.43B011.
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