Vision Transformer and Deep Sequence Learning for Human Activity Recognition in Surveillance Videos

Abstract

Human Activity Recognition is an active research area with several Convolutional Neural Network (CNN) based features extraction and classification methods employed for surveillance and other applications. However, accurate identification of HAR from a sequence of frames is a challenging task due to cluttered background, different viewpoints, low resolution, and partial occlusion. Current CNN-based techniques use large-scale computational classifiers along with convolutional operators having local receptive fields, limiting their performance to capture long-range temporal information. Therefore, in this work, we introduce a convolution-free approach for accurate HAR, which overcomes the above-mentioned problems and accurately encodes relative spatial information. In the proposed framework, the frame-level features are extracted via pretrained Vision Transformer; next, these features are passed to multilayer long short-term memory to capture the long-range dependencies of the actions in the surveillance videos. To validate the performance of the proposed framework, we carried out extensive experiments on UCF50 and HMDB51 benchmark HAR datasets and improved accuracy by 0.944% and 1.414%, respectively, when compared to state-of-the-art deep models.