Abnormal track analysis method based on an adjoint model

Abstract

The invention discloses an abnormal track analysis method based on an adjoint model. The method comprises the following steps that a video structuring system carries out face recognition to obtain face video data and preprocesses the face video data, then accompanying analysis mining is carried out on accompanying personnel through a frequent model mining algorithm to obtain accompanying relationship data and accompanying risk coefficients, and the accompanying relationship data and the accompanying risk coefficients are recorded as risk accompanying personnel when the accompanying risk coefficients are larger than accompanying personnel risk threshold values; accompanying track abnormity detection is performed on the accompanying relation data of the risk accompanying personnel through anunsupervised learning algorithm, a supervised learning model is trained through an automatic feature engineering algorithm based on the abnormal track of the risk accompanying personnel, and carryingout risk accompanying personnel abnormal track analysis. The method is based on the face video structured data, combines with the adjoint analysis model, analyzes the abnormal trajectory through theabnormal trajectory detection algorithm on the basis of considering the personnel adjoint relationship, and overcomes the limitation of poor accuracy and applicability of constructing the model only from the perspective of time and position.

Description

technical field [0001] The invention belongs to the technical field of video data processing, and in particular relates to an adjoint model-based abnormal trajectory analysis method. Background technique [0002] The intelligent perception of public security information is based on the crime characteristics and public security situation in a certain time and space, using the theory and method of machine learning under the background of artificial intelligence, through the classification, screening, analysis, and prediction of police information, to identify possible crimes and cause crimes. The various elements of social unrest and their symptoms are closely monitored, their development trends and degree of harm are accurately predicted, warning signs are captured, early warnings are made in a timely manner, and advance prevention is formed to effectively prevent and control the occurrence of crimes and the outbreak of major vicious cases. A set of operating mechanisms. Tra...

AI Technical Summary

Problems solved by technology

[0009] The purpose of the present invention is to: solve the deficiencies in the above-mentioned prior art, provide a kind of abnormal trajectory analysis method based on adjoint model, based on adjoint analysis and abnormal trajectory comprehensive analysis, improve the detection accuracy of abnormal trajectory; in the stage of unsupervised learning abnormal detection, Use FastDTW and improved density clustering technology to reduce system overhead and improve computing performance; use automatic feature engineering technology in the supervised learning stage to solve the difficult problem of trajectory feature selection and improve model analysis efficiency and quality

Images