Enhanced Action Recognition Using Multiple Stream Deep Learning with Optical Flow and Weighted Sum

Abstract

Various action recognition approaches have recently been proposed with the aid of three-dimensional (3D) convolution and a multiple stream structure. However, existing methods are sensitive to background and optical flow noise, which prevents from learning the main object in a video frame. Furthermore, they cannot reflect the accuracy of each stream in the process of combining multiple streams. In this paper, we present a novel action recognition method that improves the existing method using optical flow and a multi-stream structure. The proposed method consists of two parts: (i) optical flow enhancement process using image segmentation and (ii) score fusion process by applying weighted sum of the accuracy. The enhancement process can help the network to efficiently analyze the flow information of the main object in the optical flow frame, thereby improving accuracy. A different accuracy of each stream can be reflected to the fused score while using the proposed score fusion method. We achieved an accuracy of 98.2% on UCF-101 and 82.4% on HMDB-51. The proposed method outperformed many state-of-the-art methods without changing the network structure and it is expected to be easily applied to other networks.

A Dance Somersault Pose Recognition Model Using Multifeature Fusion Algorithm

“…At present, a large number of scientific research institutions and researchers have done in-depth research on this topic and achieved some good results. However, the research on the combination of action identify technology and dance movements is still in its infancy [4,5]. Due to the changeable stage background and costumes of dance movements, occlusion and self-occlusion are easy to occur in the performance process, and dance movements have high complexity and other problems, so the representation information fusion cannot accurately and completely express the information of human movements in most cases, while the static information of human bodies is often overlooked based on posture features.…”