Abstract Compressive tracking algorithms based on compressive sensing theory for reducing the dimension of Haar-like feature of the target utilize a fixed tracking scale, and therefore they are prone to tracking drift or even target missing when the size of the target changes. To overcome the drawback, the variation of Haar-like feature according to the target scales is analyzed. It is found that the values of Haar-like feature of target in the tracking rectangular frame change with the area of the tracking frame in an approximately linear way within certain range of scales. Grounded on this relationship, an improved compressive tracking algorithm adapting to variable target scales (CTVS) is proposed. Experimental results show that CTVS can adapt to the change of target size and perform well in reducing the influence of interferences like occlusion, light illumination variation, background clutter and deformation. Moreover, CTVS is capable of real-time tracking with higher robustness, accuracy and computation efficiency.
Fund:Supported by National Natural Science Foundation of China (No.61502105)
About author:: ZHANG Yuting, born in 1992, master student. Her research interests include image processing and object tracking. YE DongyiCorresponding author, born in 1964, Ph.D., professor. His research interests include computational intelligence and data mining. KE Xiao, born in 1983, Ph.D., lecturer. His research inte-rests include image processing, computer vision and machine learning. CHEN Zhaojiong, born in 1964, master, professor. Her research interests include image processing and computational intelligence.
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2016, Vol. 29 
