Anti-occlusion particle filtering target tracking method based on fusion of color features and local binary pattern features

Abstract

The invention discloses an anti-occlusion particle filtering target tracking method based on the fusion of color features and local binary pattern features. The method comprises the following steps: carrying out target initialization: carrying out feature extraction on the color integral histogram and the local binary pattern integral histogram of an area of interest; calculating the feature determinacy coefficient of the color features and the local binary pattern features; according to different current target states, selecting different tracking methods; if the target state is normal, carrying out target tracking by a particle filtering method which fuses the color features and the local binary pattern features, carrying out target tracking by a blocking particle filtering method which fuses the color features and the local binary pattern features if the target state is partial occlusion, and predicting a target position by a least square method if the target state is serious occlusion; updating the current target state; when the target is under the normal state, updating the target; resampling particles; and carrying out particle propagation. By use of the anti-occlusion particle filtering target tracking method, the stability and the robustness of the target tracking under an occlusion situation can be improved.

Description

technical field [0001] The invention relates to the fields of image processing, video processing, object tracking and the like, and in particular relates to the field of video-based object tracking. Background technique [0002] In video object tracking technology, the occlusion problem is a common difficulty. During the moving process, the target may encounter various occlusions: self-occlusion of the target due to rotation or movement, mutual occlusion caused by the moving target encountering other pedestrians, occlusion of the moving target by obstacles in the surrounding environment, etc. When the target encounters occlusion, the extraction of the target's feature information is interfered, resulting in incomplete or even no acquisition of the target feature, which eventually leads to inaccurate target tracking, and even the target is lost. [0003] Particle filtering is an algorithm simulated by Monte Carlo methods and derived based on Bayesian estimation, which uses a...

AI Technical Summary

Problems solved by technology

[0005] In order to overcome the limitation of the single feature of the existing video target tracking method and the problem of poor tracking effect and even lost target when occlusion occurs, the present invention proposes a feature fusion based on color and local binary mode. Anti-occlusion particle filter target tracking method, which uses color features and local binary pattern features to perform additive fusion through deterministic coefficients according to the degree of discrimination between their respective features and the background, which can describe target features more effectively. During the tracking process, Make real-time judgment on occlusion situations and adopt corresponding tracking mechanisms for different occlusion situations, so as to improve the stability and robustness of target tracking under occlusion situations

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