Dual linear structured support vector machine tracking method via scale correlation filter

Weisheng Li; Yanquan Chen; Bin Xiao; Chen Feng

doi:10.1117/1.JEI.27.1.013027

26 February 2018 Dual linear structured support vector machine tracking method via scale correlation filter

Weisheng Li, Yanquan Chen, Bin Xiao, Chen Feng

Author Affiliations +

Journal of Electronic Imaging, Vol. 27, Issue 1, 013027 (February 2018). https://doi.org/10.1117/1.JEI.27.1.013027

Abstract

Adaptive tracking-by-detection methods based on structured support vector machine (SVM) performed well on recent visual tracking benchmarks. However, these methods did not adopt an effective strategy of object scale estimation, which limits the overall tracking performance. We present a tracking method based on a dual linear structured support vector machine (DLSSVM) with a discriminative scale correlation filter. The collaborative tracker comprised of a DLSSVM model and a scale correlation filter obtains good results in tracking target position and scale estimation. The fast Fourier transform is applied for detection. Extensive experiments show that our tracking approach outperforms many popular top-ranking trackers. On a benchmark including 100 challenging video sequences, the average precision of the proposed method is 82.8%.

Citation Download Citation

Weisheng Li, Yanquan Chen, Bin Xiao, and Chen Feng "Dual linear structured support vector machine tracking method via scale correlation filter," Journal of Electronic Imaging 27(1), 013027 (26 February 2018). https://doi.org/10.1117/1.JEI.27.1.013027

Received: 14 May 2017; Accepted: 23 January 2018; Published: 26 February 2018

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