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Object tracking algorithm based on multiple features cascade


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Object tracking algorithm based on multiple features cascade
Xu TianyangWu Xiaojun
School of IoT Engineering,Jiangnan University,Wuxi 214122,China
multiple features cascade object tracking algorithm particle filter log-Gabor filter local binary pattern oriented gradients histograms frequency response Gaussian mixture model posterior probability distribution
In order to improve the effectiveness of visual object tracking,an object tracking algorithm based on multiple features cascade is proposed with particle filters as a tracking frame.Invalid particles are removed by log-Gabor filters as a discrimination stage; particle tracking is realized by cascade particle weighting stage combing log-Gabor features,local binary pattern(LBP)features and histograms of oriented gradients(HOG)features.The particles are estimated totally by the frequency response of log-Gabor filters,so that their effectiveness is decided,and the frequency domain characters are outputted by the log-Gabor filters.The total information and detail information are handled by considering LBP and HOG local features.The peak valve of posterior probability distribution is protruded using Gaussian mixture model(GMM).Experimental results indicate that the proposed method can remove invalid particles efficiently and realize robust tracking effectively.


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Last Update: 2015-06-30