Hybrid features-enabled dragon deep belief neural network for activity recognition

Abstract

ABSTRACTActivity recognition is a challenging task in computer vision that finds widespread applications in various fields, such as motion capture, video retrieval, security, and video surveillance. The objective of this work is to present a technique for recognizing human activities in videos using Dragon Deep Belief Network (DDBN) and hybrid features, which comprises of features like shape, coverage factor, and Space-Time Interest (STI) points. Initially, the keyframes from the input video sequence are extracted using Structural Similarity (SSIM) measure. Then, the features, such as shape, coverage factor, and STI points, are extracted from the keyframes. Based on the feature vector extracted, the proposed DDBN classifier, which is designed by the effective combination of DBN and Dragonfly Algorithm (DA), a classification on human activities, such as walk, bend, etc. in videos. In DDBN, the weights in the network are selected optimally using DA. The weight update using the DA for each incoming feature improves the performance of the DDBN classifier. Further it improves the accuracy in classification of actions. The proposed DDBN classifier is experimented using KTH and Weizmann datasets based on three evaluation parameters, such as accuracy, sensitivity, and specificity. From the performance evaluation, the proposed DDBN classifier could attain better performance with the probability of 98.5% accuracy, 0.96 sensitivity, and 0.959 specificity, respectively.