Masked man event automatic detection method based on skin color characteristics

Abstract

The invention provides a masked man event automatic detection method based on skin color characteristics. According to the method, after a video file to be processed is read, a video image is firstly zoomed, color images in a video are converted into grayscale images, then an object motion foreground is obtained by use of a frame difference method, next, a motion object rectangular box is obtained, then head detection is carried out, face detection is performed in a head area, next, skin color segmentation is performed on the same area of an original image, and finally a masked man judgment is made. A user selects a source video and carries out masked man detection on the video by use of the method provided by the invention so that the problem is solved that video monitoring cannot actively detect illegal activities of a masked man. Since the method does not have specific requirements for a video scene, worries about replacement of a different masked man detection technology due to different scenes are unnecessary. Besides, multiples methods are used for greatly reducing a target scope under the condition that detection accurate is not lost, and the method provided by the invention is simple in calculation and can be applied to a real-time environment.

Description

technical field [0001] The invention generally relates to the field of computer intelligent monitoring video processing, in particular to an automatic detection method for masked person events based on skin color features. Background technique [0002] In recent years, with the rapid progress of society, the rapid development of science and technology, and the continuous strengthening of national strength, various high-tech criminal methods have emerged in an endless stream, whether in large or collective places such as banks, enterprises, transportation, communities, and military, or personal residences , the requirements for safety precautions are increasing day by day, so video surveillance has been widely used in all aspects of life. Although video surveillance has been widely used in public places such as shopping malls, customs, stations, banks, etc., in fact, video processing still requires a lot of manual work, and the above-mentioned on-site recorded videos are all ...

AI Technical Summary

Problems solved by technology

Although video surveillance has been widely used in public places such as shopping malls, customs, stations, banks, etc., in fact, video processing still requires a lot of manual work, and the above-mentioned on-site recorded videos are all unprocessed original videos, which cannot be obtained. Timely autonomous feedback and useful information, so it is usually only used for evidence collection after the event, and the real-time and autonomy of video surveillance are not fully utilized

Moreover, for a video, it can take more than ten hours, dozens of hours, or even hundreds of hours. time consuming

Although the field of video surveillance is also developing rapidly, it still cannot meet people's needs. Therefore, automating video surveillance, giving full play to the autonomy and real-time nature of video surveillance, and liberating artificial labor will have a great impact on the security field and even the improvement of people's living standards. Crucial role

[0003] In real life, many occasions with high security requirements require real-time monitoring of suspicious persons appearing on the scene, or re-examination of the video of the incident after the incident, which involves the detection and identification of suspicious persons such as masked people, but The redundant information of the video makes the workload of detection and case detection extremely huge. However, the existing intelligent video surveillance technology has slow processing speed and low recognition rate, which cannot meet the requirements of real-time monitoring or cannot be applied to actual scenarios.

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