Pose guided dynamic image network for human action recognition in person centric videos

Abstract

The most emerging concerns in computer vision are size of data to process and privacy preserving of the end user. Camera sensors are all around us these days, recording and analysing our day-to-day activities. In this scenario the privacy perseverance becomes a question of concern especially in case of devices working on the basis of human action recognition (HAR). Another important concern in computer vision is the size of data. The surveillance requires continues transfer of huge amount of data through the network. The processing time required to transfer the video to central server and analyses the video directly depends on the resolution of the video. The research in computer vision is exploring the possibility of working on different aspects of videos such as using only pose information or representing whole video using a single frame for the purpose of HAR. Here, an attempt is made to explore the concept of pose estimation and video representation using dynamic image to solve the dual purpose of privacy preserving and decreasing the load on network for transfer of videos over the network for analysis. In this paper, a new Pose Guided Dynamic Image (PDI) network is proposed for HAR which is capable of providing a summarized single frame for the person’s activity in any given video. Unlike dynamic image network, this approach considers only the person’s motion and discards the background motion. Therefore, PDI provides more specific information required for HAR as compared to the dynamic image. Also, by summarizing the video, the identity of the person remains masked. The proposed method is able to provide better result on both of the benchmark datasets used namely JHMDB and UCF-sports for the experimentation.