Video-based people number anomaly detection method

Abstract

The invention provides a video-based people number anomaly detection method, comprising the steps: S1, collecting a sample, and training a personnel detection model; training a yolo model under a darknet framework by labeling personnel in a sample; S2, setting a monitoring area and allowable people number abnormal duration; S3, preprocessing the video image of the monitoring area, and removing image noise points; and S4, detecting the image by using a deep learning Yolo model to obtain the position and confidence of the personnel in the area. The video-based people number anomaly detection method achieves the balance of indexes in effect and performance by tailoring the original yolo network, and solves the problem of poor performance of the original network is solved. By utilizing a largenumber of samples and sample enhancement, the video-based people number anomaly detection method increases diversity of the samples, improves the detection effects of various people, and reduces theinfluence of false detection on people number abnormality alarm by setting a calculation method for people number abnormality duration.

Description

technical field [0001] The invention belongs to the technical field of video monitoring, and in particular relates to a video-based method for detecting an abnormal number of people. Background technique [0002] With the progress and development of society, sometimes the abnormal number of people in the area is often an important signal of abnormal events. Many key places have almost achieved no blind spot monitoring, but in the face of hundreds or even thousands of video walls, supervisors It is often easy to be distracted, and it is impossible to find out the abnormal number of people in time. Therefore, video-based anomaly detection technology is very important. [0003] Existing crowd anomaly detection technology usually detects people in the image, counts the number of people, and judges whether the number of people is abnormal. However, the clarity of the recorded video is different, and the angles and postures of the people in the video are various, and the distanc...

AI Technical Summary

Problems solved by technology

However, the clarity of the recorded video is different, and the angles and postures of the people in the video are various, and the distance is also different. Inaccurate detection results of people will lead to abnormal detection results and normal detection results. The existing technology cannot guarantee abnormal detection of the number of people. the correct rate, it is impossible to make an alarm for the correct abnormal number of people

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