Fast correlation neighborhood feature point-based sliding window target tracking method and system

Abstract

The invention discloses a fast correlation neighborhood feature point-based sliding window target tracking method. The method includes the following steps of: S1, target window template generation; S2, fast correlation neighborhood feature point extraction; S3, optimal point of interest screening; S4, point of interest sliding window search; S5, feature point template matching update; and 6, decision voting output. The invention also discloses a fast correlation neighborhood feature point-based sliding window target tracking system. With the method and system of the invention adopted, the problem of poor real-time performance and low stability of target tracking in a complex condition can be solved. According to the method and system, fast correlation neighborhood feature points, adopted as point of interests, are detected, and therefore, the robustness of target feature description in a complex condition can be enhanced; point of interest screening is carried out through using a window cross-correlation relation, and therefore, the accuracy of the target description can be improved; and sliding window search and adaptive multi-scale template matching online update are adopted in template construction, and target window output is realized through adopting decision voting, and therefore, the accuracy and stability of target tracking in the complex condition can be improved.

Description

technical field [0001] The invention relates to image processing technology, in particular to a sliding window target tracking method and system based on fast correlation neighborhood feature points. Background technique [0002] Object tracking technology has a wide range of applications in the fields of image processing and computer vision, such as abnormal behavior monitoring, traffic flow monitoring, target behavior analysis and other fields. In the real scene, due to the change of the target shape, the sudden change of the external environment of the scene and the interference of multiple targets in the scene, it brings many difficulties to the real-time performance and robustness of target tracking. [0003] At present, researchers at home and abroad have conducted a lot of research on target tracking technology. Qi Zhiquan et al. proposed a template feature selection method in the patent "A Template Feature Selection Method for Target Tracking", which can effectively ...

AI Technical Summary

Problems solved by technology

[0003] At present, researchers at home and abroad have conducted a lot of research on target tracking technology. Qi Zhiquan et al. proposed a template feature selection method in the patent "A Template Feature Selection Method for Target Tracking", which can effectively eliminate non-target objects in the target template. Feature points, but the target tracking robustness is poor when applied to complex scenes

Chang Faliang et al. proposed an edge matching algorithm and multi-block grayscale matching in the paper "Target Tracking Algorithm Based on Feature Correlation Matching under Occlusion", which solved the problem of target tracking under partial occlusion. stable tracking

Alex Bewley et al. proposed an online real-time target tracking method in the paper "Simple online and realtime tracking", using the FrRCNN framework to construct multi-target detection and tracking. This method is difficult to guarantee tracking performance in complex scenes and severe lighting changes.

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