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가중 변별 상관필터를 이용한 영상 추적

Visual Tracking using Weighted Discriminative Correlation Filter

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참고문헌

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주제분류컴퓨터학

초록

In this paper, we propose the novel tracking method which uses the weighted discriminative correlation filter (DCF). We also propose the PSPR instead of conventional PSR as tracker performance evaluation method. The proposed tracking method uses multiple DCF to estimates the target position. In addition, our proposed method reflects more weights on the correlation response of the tracker which is expected to have more performance using PSPR. While existing multi-DCF-based tracker calculates the final correlation response by directly summing correlation responses from each tracker, the proposed method acquires the final correlation response by weighted combining of correlation responses from the selected trackers robust to given environment. Accordingly, the proposed method can provide high performance tracking in various and complex background compared to multi-DCF based tracker. Through a series of tracking experiments for various video data, the presented method showed better performance than a single feature-based tracker and also than a multi-DCF based tracker.

Abstract

In this paper, we propose the novel tracking method which uses the weighted discriminative correlation filter (DCF). We also propose the PSPR instead of conventional PSR as tracker performance evaluation method. The proposed tracking method uses multiple DCF to estimates the target position. In addition, our proposed method reflects more weights on the correlation response of the tracker which is expected to have more performance using PSPR. While existing multi-DCF-based tracker calculates the final correlation response by directly summing correlation responses from each tracker, the proposed method acquires the final correlation response by weighted combining of correlation responses from the selected trackers robust to given environment. Accordingly, the proposed method can provide high performance tracking in various and complex background compared to multi-DCF based tracker. Through a series of tracking experiments for various video data, the presented method showed better performance than a single feature-based tracker and also than a multi-DCF based tracker.

참고문헌 (12)

  1. A. W. M. Smeulders, "Visual tracking : An experimental survey", IEEE Trans. on Pattern Analysis and Machine Intelligence, 36(7), pp.1442~1468 (2014)
  2. 정도욱, "A Study on Improving the Adaptive Background Method for Outdoor CCTV Object Tracking System", 한국컴퓨터정보학회논문지, 20(7), pp.17~24 (2015), 한국컴퓨터정보학회
  3. 이경호, "색상과 얼굴 특징 정보를 이용한 얼굴 추적", 한국컴퓨터정보학회논문지, 18(11), pp.167~174 (2013), 한국컴퓨터정보학회
  4. M. Kristan, "The visual object tracking vot2014 challenge results", European Conference on Computer Vision Workshop, pp.1~27 (2014)
  5. D. S. Bolme, "Average of synthetic exact filters", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.2105~2112 (2009)
  6. D. S. Bolme, "Visual object tracking using adaptive correlation filters", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.2544~2550 (2010)
  7. J. F. Henriques, "Exploiting the circulant structure of tracking-by-detection with kernels", European Conference on Computer Vision, pp.702~715 (2012), Springer
  8. M. Danelljan, "Adaptive color attributes for real-time visual tracking", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.1090~1097 (2014)
  9. J. F. Henriques, "High-speed tracking with kernelized correlation filters", IEEE Trans. on Pattern Analysis and Machine Intelligence, 37(3), pp.583~596 (2015)
  10. M. Danelljan, "Accurate scale estimation for robust visual tracking", British Machine Vision Conference, pp.65.1~65.11 (2014)
  11. Y. Wu, "Online object tracking : A benchmark", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.2411~2418 (2013)
  12. P. Felzenszwalb, "A discriminatively trained, multiscale, deformable part model", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.1~8 (2008)

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