Human Detection and Segmentation Using Automatic Geodesic Active Contours for Vision Based Activity Recognition Applications

Vision-based activity recognition applications are in need of Automatic human detection and segmentation for surveillance purposes. Though there are many state-of-art methods in the literature, there still exist many challenges such as self-occlusion, illumination variations and sensitive to light conditions, appearance, and variations due to clothing. In this paper, a new novel framework for automatic detection and segmentation of the human region in a video sequence using Automatic Geodesic Active Contours is proposed. Normally geodesic active contours have static Region of coincidence but in this work, a dynamic Region of coincidence is proposed to draw the initial contours on the human region. To detect the human region the histogram of oriented gradients are computed and trained using SVM classifier. Once the human region is detected, the contour is drawn on the human region alone to segment the human region from the background. The proposed algorithm achieves an accuracy rate of 98%, 99% and 98% for KTH, Weizmann and U t-interaction dataset respectively.