Multi-element suspicious person identification method based on video feature learning

Abstract

The invention discloses a multi-element suspicious person recognition method based on video feature learning, and the method comprises the steps: carrying out the perceptual hash mapping-based key frame screening of a monitoring video, separating foreground information containing a behavior main body, and positioning an area where a key part is located; carrying out dynamic and static combined feature extraction on the preprocessing result; for a static monitoring video, extracting local features such as head postures and hand postures and overall features such as walking abnormity and clothesabnormity; for the dynamic monitoring features, mainly extracting the path repetition degree, the probability that suspicious persons appear in crowd dense points and the average stay time of mark points in a monitoring environment; calculating a suspicious person identity credibility index according to the judgment standard in combination with a credibility calculation thought; and finally, dynamically screening suspicious persons and outputting discrimination information according to corresponding thresholds. According to the method, suspicious persons can be accurately and efficiently identified in controlled and uncontrolled environments, and the method has good scientificity and higher practical application value.

Description

technical field [0001] The invention belongs to the field of information technology, in particular to a multi-element suspicious person identification method based on video feature learning. Background technique [0002] With the improvement of monitoring technology, the monitoring video becomes clearer, which provides a good hardware foundation for accurately identifying suspicious persons with criminal motives in a short period of time through video monitoring. At the same time, it also makes suspicious identification change from a controlled environment to an uncontrolled environment, and the identification effect is also uncontrollable. Generally speaking, the reasons for the unsatisfactory recognition effect are roughly divided into the following two aspects: [0003] First of all, the environment used for identity recognition is no longer controlled, and there are dynamic transformation problems such as occlusion and posture when collecting the information of the subj...

AI Technical Summary

Problems solved by technology

[0003] First of all, the environment used for identity recognition is no longer controlled, and there are dynamic transformation problems such as occlusion and posture when collecting the information of the subject. In addition, the identity information of the subject does not necessarily exist in the identity database, which may lead to extraction The feature information cannot meet the task requirements of suspicious person identification;

[0004] Secondly, the amount of surveillance video information is huge, and the processing time is long, which seriously affects the real-time performance of the system.

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