Computer Vision based Visual Activity Classification through Deep Learning Approaches

Abstract

Activity recognition is a challenging and practical research problem in computer vision. A good number of applications are approached by researchers in this area, like activity monitoring in homes, smart healthcare systems, surveillance & monitoring for security, and more. Literature around this problem provides some solutions where machine learning approaches have profoundly been utilized. This paper proposes a method based on deep neural networks to classify and recognize human activities for video surveillance applications. In order to achieve our objective, we ensemble the two different specialized neural network models, viz convolutional neural network (CNN) and recurrent neural network (RNN), to perform activity classification. Here, CNN extracts relevant spatial features from the video sequence and RNN creates a temporal relationship among those spatial features, which in turn leads to activity classification. To evaluate the effectiveness of the proposed method, we use precision, recall and accuracy metrics. On evaluation, the method is found to be more efficient in classifying video sequences, with an accuracy of up to 91.90%.