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Visual Tracking via Spatially Aligned Correlation Filters Network
Zhang, Mengdan1; Wang, Qiang1; Xing, Junliang1; Gao, Jin1; Peng, Peixi1; Hu, Weiming1; Maybank, Steve2
Conference Name15th European Conference on Computer Vision, ECCV 2018
Conference DateSeptember 8, 2018 - September 14, 2018
Conference PlaceMunich, Germany

Correlation filters based trackers rely on a periodic assumption of the search sample to efficiently distinguish the target from the background. This assumption however yields undesired boundary effects and restricts aspect ratios of search samples. To handle these issues, an end-to-end deep architecture is proposed to incorporate geometric transformations into a correlation filters based network. This architecture introduces a novel spatial alignmentmodule, which provides continuous feedback for transforming the target from the border to the center with a normalized aspect ratio. It enables correlation filters to work on well-aligned samples for better tracking. The whole architecture not only learns a generic relationship between object geometric transformations and object appearances, but also learns robust representations coupled to correlation filters in case of various geometric transformations. This lightweight architecture permits real-time speed. Experiments show our tracker effectively handles boundary effects and aspect ratio variations, achieving state-of-the-art tracking results on recent benchmarks.

Indexed ByEI
Sub direction classification目标检测、跟踪与识别
planning direction of the national heavy laboratory实体人工智能系统感认知
Paper associated data
Document Type会议论文
Affiliation1.CAS Center for Excellence in Brain Science and Intelligence Technology, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, China
2.Birkbeck College, University of London, London, United Kingdom
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Zhang, Mengdan,Wang, Qiang,Xing, Junliang,et al. Visual Tracking via Spatially Aligned Correlation Filters Network[C],2018.
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