Abnormal event detection in tourism video based on salient spatio-temporal features and sparse combination learning

摘要

With the booming development of tourism, travel security problems are becoming more and more prominent. Congestion, stampedes, fights and other tourism emergency events occurred frequently, which should be a wake-up call for tourism security. Therefore, it is of great research value and application prospect to real-time monitor tourists and detect abnormal events in tourism surveillance video by using computer vision and video intelligent processing technology, which can realize the timely forecast and early warning of tourism emergencies. At present, although most of the video-based abnormal event detection methods work well in simple scenes, there are often problems such as low detection rate and high false positive rate in complex motion scenarios, and the detection of abnormal events can't be processed in real time. To tackle these issues, we propose an abnormal event detection model in tourism video based on salient spatio-temporal features and sparse combination learning, which has good robustness and timeliness in complex motion scenarios and can be adapted to real-time anomaly detection in practical applications. Specifically, spatio-temporal gradient model is combined with foreground detection to extract 3D gradient features on the foreground target of video sequence as the salient spatio-temporal features, which can eliminate the interference of the background. Sparse combination learning algorithm is used to establish the abnormal event detection model, which can realize the real-time detection of abnormal events. In addition, we construct a new ScenicSpot dataset with 18 video clips (5964 frames) containing both normal and abnormal events. The experimental results on ScenicSpot dataset and two standard benchmark datasets show that our method can realize the automatic detection and recognition of tourists' abnormal behavior, and has better performance compared with the classical methods.