Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Target Image Exchange Model for Object Tracking Based on Siamese Network

샴 네트워크 기반 객체 추적을 위한 표적 이미지 교환 모델

  • Park, Sung-Jun (School of Electronics and Information Engineering, Korea Aerospace University) ;
  • Kim, Gyu-Min (School of Electronics and Information Engineering, Korea Aerospace University) ;
  • Hwang, Seung-Jun (School of Electronics and Information Engineering, Korea Aerospace University) ;
  • Baek, Joong-Hwan (School of Electronics and Information Engineering, Korea Aerospace University)
  • Received : 2021.02.05
  • Accepted : 2021.02.19
  • Published : 2021.03.31
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Abstract

In this paper, we propose a target image exchange model to improve performance of the object tracking algorithm based on a Siamese network. The object tracking algorithm based on the Siamese network tracks the object by finding the most similar part in the search image using only the target image specified in the first frame of the sequence. Since only the object of the first frame and the search image compare similarity, if tracking fails once, errors accumulate and drift in a part other than the tracked object occurs. Therefore, by designing a CNN(Convolutional Neural Network) based model, we check whether the tracking is progressing well, and the target image exchange timing is defined by using the score output from the Siamese network-based object tracking algorithm. The proposed model is evaluated the performance using the VOT-2018 dataset, and finally achieved an accuracy of 0.611 and a robustness of 22.816.

본 논문에서는 샴 네트워크 기반의 객체 추적 알고리즘의 성능 향상을 위한 표적 이미지 교환 모델을 제안한다. 샴 네트워크 기반의 객체 추적 알고리즘은 시퀀스의 첫 프레임에서 지정된 표적 이미지만을 사용하여 탐색 이미지 내에서 가장 유사한 부분을 찾아 객체를 추적한다. 첫 프레임의 객체와 유사도를 비교하기 때문에 추적에 한 번 실패하게 되면 오류가 축적되어 추적 객체가 아닌 부분에서 표류하게 되는 현상이 발생한다. 따라서 CNN(Convolutional Neural Network)기반의 모델을 설계하여 추적이 잘 진행되고 있는지 확인하고 샴 네트워크 기반의 객체 추적 알고리즘에서 출력되는 점수를 이용하여 표적 이미지 교환 시기를 정의하였다. 제안 모델은 VOT-2018 데이터 셋을 이용하여 성능을 평가하였고 최종적으로 정확도 0.611 견고도 22.816을 달성하였다.

Keywords

References

  1. Q. Wang, L. Zhang, L. Bertinetto, W. Hu, and P. H. Torr, "Fast online object tracking and segmentation: A unifying approach," in Proceedings of the IEEE/CVF conference on Computer Vision and Pattern Recognition, California: CA, pp. 1328-1338, 2019.
  2. M. Mulller, A. Bibi, S. Giancola, S. Alsubaihi, and B. Ghanem, "Trackingnet: A large-scale dataset and benchmark for object tracking in the wild," in Proceedings of the European Conference on Computer Vision, Munich: MUC, pp. 300-317, 2018.
  3. M. Kristan and other 155th authors, "The sixth visual object tracking vot2018 challenge results," in Proceedings of the European Conference on Computer Vision, Munich: MUC, pp. 3-53, 2018.
  4. G. Koch, R. Zemel, and R. Salakhutdinov, "Siamese neural networks for one-shot image recognition," in International Conference on Machine Learning deep learning workshop, Lille: LIL, pp. 1-8, 2015.
  5. L. Bertinetto, J. Valmadre, J. F. Henriques, A. Vedaldi, and P. H. Torr, "Fully-convolutional siamese networks for object tracking," in Proceedings of the European Conference on Computer Vision, Amsterdam: AMS, pp. 850-856, 2016.
  6. B. Li, J. Yan, W. Wu, Z. Zhu, and X. Hu, "High performance visual tracking with siamese region proposal network," in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, Salt Lake City: SLC, pp. 8971-8980, 2018.
  7. K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, Las Vegas: LAS, pp. 770-778, 2016.
  8. D. Held, S. Thrun, and S. Savarese, "Learning to track at 100 at fps with deep regression networks," in Proceedings of the European Conference on Computer Vision, Amsterdam: AMS, pp.749-756, 2016.