Long-term scale adaptive tracking with kernel correlation filters

Yueren Wang; Hong Zhang; Lei Zhang; Yifan Yang; Mingui Sun

doi:10.1117/12.2303390

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10 April 2018 Long-term scale adaptive tracking with kernel correlation filters

Yueren Wang, Hong Zhang, Lei Zhang, Yifan Yang, Mingui Sun

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Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 1061504 (2018) https://doi.org/10.1117/12.2303390
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China

Abstract

Object tracking in video sequences has broad applications in both military and civilian domains. However, as the length of input video sequence increases, a number of problems arise, such as severe object occlusion, object appearance variation, and object out-of-view (some portion or the entire object leaves the image space). To deal with these problems and identify the object being tracked from cluttered background, we present a robust appearance model using Speeded Up Robust Features (SURF) and advanced integrated features consisting of the Felzenszwalb's Histogram of Oriented Gradients (FHOG) and color attributes. Since re-detection is essential in long-term tracking, we develop an effective object re-detection strategy based on moving area detection. We employ the popular kernel correlation filters in our algorithm design, which facilitates high-speed object tracking. Our evaluation using the CVPR2013 Object Tracking Benchmark (OTB2013) dataset illustrates that the proposed algorithm outperforms reference state-of-the-art trackers in various challenging scenarios.

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Yueren Wang, Hong Zhang, Lei Zhang, Yifan Yang, and Mingui Sun "Long-term scale adaptive tracking with kernel correlation filters", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 1061504 (10 April 2018); https://doi.org/10.1117/12.2303390

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