Using color bin images for crowd detections
Chern-Horng Sim, Rajmadhan Ekambaram, Surendra Ranganath · 2008
In this paper, we propose a method to reduce the false alarm rate or alternatively to improve the detection rate of a local detector for individuals within dense crowds. The detected windows from a Viola-type head detector are processed in a second pass by a cascade of boosted classifiers working with Haar-like features to improve performance. The latter classifier uses color bin images, constructed from normalized rg color histograms of detected windows. Experimental results show a reduction in false alarm rate from 35.9% obtained by the basic detector to 23.9% after the second pass with our approach. This high reduction in false alarm rate was accompanied by only a small reduction in true detections from 87.3% to 82.5%.