Sparse joint model target tracking method based on self-adaptive selection mechanism

Abstract

The invention discloses a sparse joint model target tracking method based on a self-adaptive selection mechanism. When a sparse judgment model is constructed, more discriminatory features are extracted by use of a feature selection mechanism, confidence value measurement is taken as a constraint, and the target and the background can be better distinguished; when a sparse generation model is constructed, in combination with L1 regularization and PCA subspace reconstitution concept, the target not only reserves sufficient appearance information, but also can effectively resist outlier disturbance, and an iterative algorithm combining linear regression and soft threshold operators is proposed for the minimum solution of a target function. Compared with a conventional multiplicative combined mechanism, the self-adaptive selection mechanism based on the Euclidean distance is proposed, the deviation is calculated by comparing the difference between prediction results of the two models and the tracking result of the previous frame, whether the models degenerate is judged, and the more reasonable joint model evaluation function is constructed to improve the tracking accuracy.

Description

Technical field: [0001] The invention belongs to the field of machine vision, in particular to a sparse joint model target tracking method based on an adaptive selection mechanism. Background technique: [0002] As one of the research hotspots in the field of computer vision, target tracking technology aims to detect, extract, identify and track target objects in a series of images, so as to obtain relevant parameters of the target object, such as position, speed, scale, trajectory, etc.; Further process and analyze based on the tracking results to understand the behavior of the target object or complete higher-level tasks. Its research results have good application value in video surveillance, traffic monitoring, medical diagnosis, military strike, human-computer interaction, etc. Although target tracking technology has broad application prospects and research value, and researchers have made a lot of research progress in recent years, it is still very challenging to desig...

AI Technical Summary

Problems solved by technology

However, when one model deteriorates or is lost during the tracking process, this direct multiplication mechanism will lead to error accumulation, which in turn deteriorates the performance of the entire joint model.

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