Human Area Refinement for Human Detection

Human detection technologies are very useful tools to understand human activity for various purposes, such as surveillance. Recently, tracking-by-detection methods have also become popular for analyzing human activity, but their performance is greatly affected by the accuracy of detected human areas because they use online learning based on the detected results. In order to improve the performance of such tracking methods, the inclination of human bodies in the image is considered as a way to refine the detected human bounding boxes. Based on background subtraction and a novel scheme of estimating human foot position, a refinement scheme is proposed to estimate a bounding box more accurately, which can better fit the contours of inclined human bodies than the conventional method. Experimental results illustrated that the bounding boxes refined by the proposed algorithm achieved a higher cover rate of 92.7 % and a smaller mean angle error of 0.7° compared with the cover rate of 83.7 % and mean angle error of 3.8° obtained using the conventional method, as determined by comparison with the ground truth, and a real-time detection speed of 32.3 fps on a 640 × 480 video has been realized. Thus, tracking performance is significantly enhanced by refining the human areas, with a mean improvement of 42.4 % in the F-measure when compared with the conventional method.