Multi-level characteristic data association-based multi-target visual tracking method

Abstract

The invention belongs to the field of security and protection monitoring and relates to a multi-level characteristic data association-based method for detecting and tracking multiple targets in a monitored scene by adopting a multi-target visual tracking algorithm. The method comprises the following steps: acquiring target objects, and extracting targets from a video scene background to serve as to-be-tracked objects; establishing target models, extracting global features and local features of the targets to serve as features of described target object models, and removing the targets with very low matching degree by filtering; performing association calculation by taking target similarity, texture similarity and local color similarity as the basis, and performing a matching strategy from two levels of the global features and the local features; processing target occlusion by adopting an occlusion judging mechanism; and obtaining target trajectories which are longer and comparatively accurate according to an association algorithm of subsequent iteration. According to the method provided by the invention, not only is the accuracy of target tracking improved but also the occlusion issue among the targets is effectively solved. Compared with an association tracking algorithm only using overall feature modeling, the method provided by the invention is higher in accuracy.

Description

Technical field [0001] The invention belongs to the field of security monitoring and computer vision, and relates to a method for detecting and tracking multiple targets in a monitoring scene based on a multi-target visual tracking algorithm based on multi-level feature data association. Background technique [0002] In recent years, with the continuous development of security monitoring technology, the application of video monitoring in social life has become more and more extensive, and the requirements for monitoring systems in traffic management, traffic accident analysis, community security, bank monitoring, and social stability are constantly increasing. Target tracking is the core function of intelligent video surveillance. By tracking the monitored target, its motion trajectory can be obtained or its motion behavior can be further analyzed to provide reliable support for intelligent monitoring. [0003] At present, target tracking algorithms have been widely used in actual ...

AI Technical Summary

Problems solved by technology

In the process of multi-target movement, the occlusion between objects will greatly affect the tracking effect, and the addition of color features solves the partial occlusion problem, but a single feature application still cannot meet the requirements of accurate tracking

In addition, there are infrared and multi-camera auxiliary monitoring methods to achieve multi-target tracking research. Although the effect is obvious, the hardware environment built in the scene is very demanding.

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