Classification of Videos Based on Deep Learning

Abstract

Automatic classification of videos is a basic task of content archiving and video scene understanding for broadcasters. And the time series modeling is the key to video classification. To solve this problem, this paper proposes a new video classification method based on temporal difference networks (TDN), which focuses on capturing multiscale time information for effective action classification. The core idea of TDN is to design an effective time module by clearly using the time difference operator and systematically evaluate its impact on short-term and long-term motion modeling. In order to fully capture the time information of the entire video, TDN has established a two-level difference model. For local motion modeling, the time difference in consecutive frames is used to provide a more refined motion mode for convolutional neural network (CNN). For global motion modeling, the time difference of the segments is combined to capture the remote structure for the extraction of the motion feature. The experimental results on two public video anomaly detection data sets, namely, UCF sports data set and SVW field sports data set, prove that the performance of the proposed method is better than some existing methods.