基于粒子滤波的on-line boosting目标跟踪算法

Abstract

Abstract: Object tracking is regarded as a classification between object and background in on-line boosting tracking algorithm (HBT) based on the Haar-like feature.The new position of the object is obtained by searching the maximum classification confidence in the candidate region.However,the exhaustive search procedure makes it difficult to ensure real-time property and result in tracking lost easily when the size of the object is too big or the speed of the object is too fast to get the maximum confidence in the candidate regions.In this paper,the particle filter is introduced into the HBT object tracking framework and an algorithm of on-line boosting object tracking based on particle filter (PFHBT) is proposed:the motion of the object is modeled and the object classification confidence is regarded as the observation of particle filter.Experimental results show that the algorithm not only improves the computing speed significantly,but also solves the problem of tracking lost caused by object fast moving effectively.

Key words: object tracking, on-line boosting, particle filter, confidence, motion model

CLC Number:

TP391.41

MA Xian-bing, SUN Shui-fa, QIN Yin-shi, GUO Qing, XIA Ping. Object Tracking Algorithm of On-line Boosting Based on Particle Filter[J].Journal of Guangxi Normal University(Natural Science Edition), 2013, 31(3): 100-105.

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Object Tracking Algorithm of On-line Boosting Based on Particle Filter

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