Deep Positional Attention-Based Hierarchical Bidirectional RNN with CNN-Based Video Descriptors for Human Action Recognition.

Human Action Recognition (HAR) is a highly notable area of study in contemporary computer vision. Many investigations focused on recognizing a person's actions from video streams based on extracting features regarding orientation and motion. This article presents a Joints and Trajectory-pooled 3D-Deep Positional Attention (PA)-based Hierarchical Bidirectional Recurrent Convolutional Descriptors (JTDPAHBRD) approach which uses a PA-based Hierarchical Bidirectional Recurrent Neural Network (PAHBRNN) for enhancing the feature aggregation process. First, the entire video is segregated into multiple blocks and they are provided to the 2-stream bilinear Convolutional 3D (C3D) model which applies the PAHBRNN as feature aggregation. In PAHBRNN, the feature vectors related to the different parts of a human skeleton in a certain clip are hierarchically aggregated using the position-aware guidance vector. Then, 2 different streams in the C3D network are fused and trained end-to-end using the softmax loss to get the final video descriptor for a particular video sequence. Further, the Support Vector Machine (SVM) classifier is applied to classify the resultant video descriptor to recognize the person's actions. At last, the investigational outcomes demonstrate the JTDPAHBRD achieves 99.6% better recognition accuracy than the classical state-of-the-art approaches. [ABSTRACT FROM AUTHOR]