Object and motion cues based collaborative approach for human activity localization and recognition in unconstrained videos

Abstract

This paper addresses the problem of activity localization and recognition in large scale video datasets by the collaborative use of holistic and motion based information (called motion cues). The concept of salient objects is used to obtain the holistic information while the motion cues are obtained by affine motion model and optical flow. The motion cues compensate the camera motion and localize the object of interest in a set of object proposals. Furthermore, the holistic information and motion cues are fused to get a reliable object of interest. In recognition phase, the holistic and motion based features are extracted from the object of interest for the training and testing of classifier. The extreme learning machine is adopted as a classifier to reduce the training and testing time and increase the classification accuracy. The effectiveness of the proposed approach is tested on UCF sports dataset. The detailed experimentation reveals that the proposed approach performs better than state-of-the-art action localization and recognition approaches.