IEMEK Journal of Embedded Systems and Applications (대한임베디드공학회논문지)

Institute of Embedded Engineering of Korea (대한임베디드공학회)
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Visual object tracking using inter-frame correlation of convolutional feature maps

컨볼루션 특징 맵의 상관관계를 이용한 영상물체추적

  • Received : 2016.06.29
  • Accepted : 2016.07.21
  • Published : 2016.08.31
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Abstract

Visual object tracking is one of the key tasks in computer vision. Robust trackers should address challenging issues such as fast motion, deformation, occlusion and so on. In this paper, we therefore propose a visual object tracking method that exploits inter-frame correlations of convolutional feature maps in Convolutional Neural Net (ConvNet). The proposed method predicts the location of a target by considering inter-frame spatial correlation between target location proposals in the present frame and its location in the previous frame. The experimental results show that the proposed algorithm outperforms the state-of-the-art work especially in hard-to-track sequences.

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References

  1. A. Yilmaz, O. Javed, M. Shah, "Object Tracking: A Survey," Preceeding of ACM Computing Surveys, Vol. 38, No. 4, pp. 1-45, 2006. https://doi.org/10.1145/1132952.1132953
  2. H. Nam, B. Han, "Learning multi-domain convolutional neural networks for visual tracking," CVPR, 2016.
  3. M. Kristan, J. Matas, A. Leonardis, M. Felsberg, L. Cehovin, G. Fernandez, T. Vojir, G. Hager, G. Nebehay, R. Pflugfelder, "The Visual Object Tracking VOT2015 Challenge Results," Preceeding of International Conference on Computer Vision Workshops, pp. 1-23, 2015.
  4. Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, "Gradient-Based Learning Applied to Document Recognition," Proceedings of the IEEE, Vol. 86, No. 11, pp. 2278-2324, 1998. https://doi.org/10.1109/5.726791
  5. http://www.image-net.org
  6. A. Vedaldi, K. Lenc. "Matconvnet-convolutional neural networks for matlab," Proceedings of ACM international conference on Multimedia, pp. 689-692, 2015.
  7. Y. Wu, J. Lim, M.H. Yang, "Online object tracking: A benchmark," Preceeding of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2411-2418, 2013.