ACTION RECOGNITION USING SPATIO-TEMPORAL DIFFERENTIAL MOTION

This paper presents human action recognition using spatiotemporal differential motion maps. The concept of differential motion in space and time helps in overcoming several challenges in action recognition such as camera motion and multiple actions in the same scene. Spatially differential motion in a frame is represented using divergence of optical flow. Divergence map of each frame in a video is projected onto three orthogonal Cartesian planes. A map of spatio-temporal differential motion is formed by accumulation of the absolute differences between projected maps of pairs of consecutive frames through an entire video sequence for each projection. A feature vector is formed from these three spatiotemporal maps of differential motion which represents the action performed in the video. Classification of action was done by using l(2)-regularized collaborative representation with a distance-weighted Tikhonov matrix. We tested on two popular datasets, KTH and UCF11, and got better performance than state-of-the-art methods. A comparison of differential motion and optical flow (any motion with respect to the camera) was also done to show that differential motion gives better feature representation than simply using optical flow.