Spatio-temporal action localization and detection for human action recognition in big dataset

Human action recognition is still attracting the computer vision research community due to its various applications. However, despite the variety of methods proposed to solve this problem, some issues still need to be addressed. In this paper, we present a human action detection and recognition process on large datasets based on Interest Points trajectories. In order to detect moving humans in moving field of views, a spatio-temporal action detection is performed basing on optical flow and dense speed-up-robust-features (SURF). Then, a video description based on a fusion process that combines motion, trajectory and visual descriptors is proposed. Features within each bounding box are extracted by exploiting the bag-of-words approach. Finally, a support-vector-machine is employed to classify the detected actions. Experimental results on the complex benchmark UCF101, KTH and HMDB51 datasets reveal that the proposed technique achieves better performances compared to some of the existing state-of-the-art action recognition approaches.