Motion Boundary Trajectory for Human Action Recognition

In this paper, we propose a novel approach to extract local descriptors of a video, based on two ideas, one using motion boundary between objects, and, second, the resulting motion boundary trajectories extracted from videos, together with other local descriptors in the neighbourhood of the extracted motion boundary trajectories, histogram of oriented gradients, histogram of optical flow, motion boundary histogram, can be used as local descriptors for video representations. The motion boundary approach captures more information between moving objects which might be caused by camera movements. We compare the performance of the proposed motion boundary trajectory approach with other state-of-the-art approaches, e.g., trajectory based approach, on a number of human action benchmark datasets (YouTube, UCF sports, Olympic Sports, HMDB51, Hollywood2 and UCF50), and found that the proposed approach gives improved recognition results.