Fast Pedestrian Detection Algorithm Based on Partial Least Squares and Improved Center-symmetric CENTRIST

According to the issue of pedestrian detection in complex background, this paper presents an Improved Center-Symmetric CENTRIST (ICS_CENTRIST) feature from the view of the pedestrian edge information. This feature has characters of simple calculation and powerful description ability. It can express the pedestrian's edge contour information perfectly with only 32 dimensions. Three cascaded classifier are used for pedestrian detection. The linear SVM based on auxiliary integral image is used for excluding most non-pedestrian area quickly in the first stage. During the second and third stages, the ICS_CENTRIST features of first 12 and 21 blocks with most strong distinguishable chosen by Partial Least Squares (PLS) method are accepted respectively, and then Histogram Intersection Kernel SVM (HIK-SVM) is used for accurate detecting. Experimental results show that this algorithm can get better detection results in complex background, and the detection speed is average 50 ms for 447358 images, which is improved by 50% and 90% compared with the CENTRIST fast detecting and Histograms of Oriented Gradients (HOG) algorithm respectively and can meet the real-time requirements.