A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Fast visual object tracking via distortion-suppressed correlation filtering
Visual object tracking is a basic research unit in the construction of smart cities, it focuses on establishing a dynamic appearance model to represent the target in complex scenarios. In this paper, a distortion-suppressed correlation filtering based tracking method (DSCFT) is proposed. Our approach tackles distortions caused by spatial similarity comparison and temporal appearance updating. We establish our method under a Bayesian framework, where spatial and temporal appearance are embedded in likelihood and prior respectively. Firstly, The spatial distortion is handled by modifying weight windows and utilizing a proposal selection strategy to better track targets under fast motion and background clutters. Secondly, temporal information is retained in updating stage as a prior to represent dynamic variations of the target. Moreover, a multi-scale filtering scheme is integrated when updating the temporal appearance to boost the scale sensitivity. Experimental results dedicate the effectiveness and robustness of our DSCFT on benchmark videos.
Fast visual object tracking via distortion-suppressed correlation filtering
Visual object tracking is a basic research unit in the construction of smart cities, it focuses on establishing a dynamic appearance model to represent the target in complex scenarios. In this paper, a distortion-suppressed correlation filtering based tracking method (DSCFT) is proposed. Our approach tackles distortions caused by spatial similarity comparison and temporal appearance updating. We establish our method under a Bayesian framework, where spatial and temporal appearance are embedded in likelihood and prior respectively. Firstly, The spatial distortion is handled by modifying weight windows and utilizing a proposal selection strategy to better track targets under fast motion and background clutters. Secondly, temporal information is retained in updating stage as a prior to represent dynamic variations of the target. Moreover, a multi-scale filtering scheme is integrated when updating the temporal appearance to boost the scale sensitivity. Experimental results dedicate the effectiveness and robustness of our DSCFT on benchmark videos.
Fast visual object tracking via distortion-suppressed correlation filtering
Xu, Tianyang (author) / Wu, Xiao-Jun (author)
2016-09-01
1310673 byte
Conference paper
Electronic Resource
English
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