A Target Tracking Method Based on Nonparametric Online Clustering

Abstract

A target tracking method based on non -parameter online clustering class, the technical problem it solves is: given a video to track online targets for a specified interested target, thereby predict the specific location of the subsequent image frame goals and external rectanglesThe box, based on the small depth network learned from the deep network, extract distillation characteristics for interested targets.This feature has strong appearance expression, judgment ability and robustness.The problem of further solution of the present invention is that: non -parameter online clustering of the historical appearance of interested goals, and a target appearance template that retains historical information based on the weighted cluster centers, and use this target appearance template to generate target space distribution, thereby improvingThe target tracking accuracy, reduce the risk of target drift, and then reduce the error rate.

Description

technical field [0001] The present invention mainly relates to the fields of video image processing, intelligent video monitoring, pattern recognition and the like, and specifically relates to a target tracking method based on non-parameter online clustering. Background technique [0002] Object tracking is one of the most basic and challenging core issues in the field of intelligent video surveillance. As a relatively low-level problem in computer vision algorithms, it has broad application prospects. For example, high-level intelligent analysis applications such as motion analysis and anomaly detection in the field of intelligent video surveillance are based on target tracking. Extracting the trajectory of moving objects in the scene can provide an effective basis for higher-level intelligent analysis in the monitoring scene. [0003] Most of the traditional target tracking methods initialize a certain target of interest in the first frame of a given video, and then trai...

AI Technical Summary

Problems solved by technology

This kind of method uses the online learning mode to continuously update the appearance model during the tracking process, ignoring the fact that the target of interest is easily affected by occlusion, disappearance-reappearance, and it is easy to cause model drift.

At the same time, since the learning process of the model relies heavily on the appearance model, when the target drifts, it is difficult to re-initialize the target model, resulting in a complete failure of tracking and poor tracking results

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