Human Activity Detection Using Attention-Based Deep Network

Abstract

Nowadays, digital surveillance devices are widely implemented to collect massive volumes of data indefinitely, necessitating human monitoring to identify various activities. The requirement for smarter surveillance in this era is for artificial intelligence and computer vision technology to automatically identify normal and aberrant actions. We present a long short-term memory (LSTM)-based attention mechanism based on a pre-train convolutional neural network (CNN) that focuses on the most important characteristics in the source frame to distinguish human activities in videos. We employ the DenseNet layers to extract the prominent spatial features from frames. We input these characteristics into an LSTM to learn temporal features in video; after that, an attention technique is used to enhance performance and calculate more high-level selected activity-related patterns. The presented system was evaluated on UCF11 datasets and achieved recognition rates of 97.90%, demonstrating a substantial advancement over the current state-of-the-art (SOTA) method.