Abnormal Behavior Recognition Method Based on Video Motion Information Feature Extraction and Adaptive Enhancement Algorithm Error Backpropagation Network

Abstract

The present invention relates to an abnormal behavior recognition method based on feature extraction of video motion information and error backpropagation network (BP Adaboost) based on adaptive enhancement algorithm, comprising: firstly calculating optical flow according to adjacent image frames of the video, by horizontal direction and The optical flow in the vertical direction is used to calculate the optical flow direction, and the optical flow direction histogram is calculated with the intensity of the optical flow as the weight, and the histogram features are converted into feature attributes with probability attributes, and then training based on the normal and abnormal training samples. The classifier is obtained by adapting the error backpropagation network (BP Adaboost) of the boosting algorithm. In the test phase, before using the trained classification model, the optical flow direction histogram of the test sample is obtained according to the same calculation method as the optical flow histogram of the adjacent frame, and finally the abnormal behavior in the test sample is checked according to the classification model obtained through training and learning. identify. The invention has the characteristics of high recognition rate and low computational complexity, and can be widely used in the fields of abnormal behavior recognition and motion analysis.

Description

technical field [0001] The invention relates to image processing technology, in particular to an abnormal behavior identification method based on video motion information feature extraction and error back propagation network (BP Adaboost) based on adaptive enhancement algorithm. Background technique [0002] As we all know, in order to ensure the safety of public places, surveillance has been widely used. However, when an accident occurs, it is still difficult to detect the accident in time because the staff in the monitoring room are faced with numerous surveillance videos. For example, if a robbery occurs suddenly in a shopping mall, if it is not dealt with in time, it will have a greater impact. Therefore, public places such as shopping malls, bustling streets, railway stations, sports stadiums and other public places need intelligent monitoring. [0003] Abnormal behavior recognition in video has been widely concerned by scholars at home and abroad. Usually, we classi...

AI Technical Summary

Problems solved by technology

However, when an accident occurs, it is still difficult to detect the accident in time because the staff in the monitoring room are faced with numerous surveillance videos.

For example, if there is a sudden robbery in the mall, if it is not dealt with in time, it will have a greater impact

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