Abnormal event detection method based on low-rank adaptive sparse reconstruction

Abstract

The invention discloses an abnormal event detection method based on low-rank adaptive sparse reconstruction, and the method comprises three processes: feature extraction, training, and detection, and specifically comprises the steps: 1), extracting multi-scale three-dimensional gradient features of a video sequence; 2), carrying out the dimensional reduction of the multi-scale three-dimensional gradient features, and forming a training feature set and a test feature set; 3), initializing the remaining training features and related parameters; 4), carrying out the iteration group sparse dictionary learning of the remaining training features, and obtaining a normal mode dictionary set and the low-rank information thereof; 5), carrying out the weighted sparse reconstruction of the test features through a group sparse dictionary set and the low-rank information, which are obtained in a training process; 6), judging whether the test features are abnormal features or not according to a reconstruction error. The method irons out defects of low-rank characteristics of video data which is not fully exploited and poor detection efficiency in the technology of abnormality detection.

Description

technical field [0001] The invention relates to the fields of pattern recognition and video analysis, and more specifically, relates to an abnormal event detection method based on low-rank adaptive sparse reconstruction. Background technique [0002] Anomaly event detection in video sequences is an active research topic in computer vision and has been widely used in many applications, such as crowd monitoring, public place detection, traffic safety, and abnormal personal behavior. In the face of massive video data, the traditional manual marking of abnormal events is time-consuming and inefficient. Therefore, automated and fast anomaly detection methods for video sequences are urgently needed. [0003] Although research on anomalous event detection has made great progress in feature extraction, behavior modeling, and anomaly measurement, anomalous event detection in video sequences is still a very challenging task. First, there is no precise definition of an anomalous even...

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to solve the shortcomings of the low-rank characteristics of video data and poor detection efficiency in the above-mentioned anomaly detection technology,

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