Abnormal Behavior Detection Using Deep-Learning-Based Video Data Structuring

With the increasing number of digital devices generating a vast amount of video data, the recognition of abnormal image patterns has become more important. Accordingly, it is necessary to develop a method that achieves this task using object and behavior information within video data. Existing methods for detecting abnormal behaviors only focus on simple motions, therefore they cannot determine the overall behavior occurring throughout a video. In this study, an abnormal behavior detection method that uses deep learning (DL)-based video-data structuring is proposed. Objects and motions are first extracted from continuous images by combining existing DL-based image analysis models. The weight of the continuous data pattern is then analyzed through data structuring to classify the overall video. The performance of the proposed method was evaluated using varying parameter settings, such as the size of the action clip and interval between action clips. The model achieved an accuracy of 0.9817, indicating excellent performance. Therefore, we conclude that the proposed data structuring method is useful in detecting and classifying abnormal behaviors.