Multiple Instance Target Tracking Algorithm Based on Compressed Sense

doi:10.3969/j.issn.1006-2475.2014.07.013

Abstract

Abstract: Aiming at the problems of unstable tracking, easy to drift, obscured loss, which are produced in real-time target tracking, we propose an improved tracking algorithm for multiple instances. In the compressed sensing and real-time tracking, by adding random measurement matrix to produce new features, multiple positive and negative instances are integrated. By combining with the boosting learning method to update the feature weights and improve the confidence map estimation, we solve the problems of target drift and loss. Experimental results show that the proposed algorithm achieves better robustness and stable real-time tracking when the target moves quickly, or in conditions that the textures and lightings change seriously, as well as it is partially covered.

Key words: target tracking, compressive sensing, multiple instance, real-time tracking, drift, target occlusion

CLC Number:

TP391

CHEN Xi, DI Lan. Multiple Instance Target Tracking Algorithm Based on Compressed Sense[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2014.07.013.

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