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Target Tracking Algorithm based on Context-Aware Deep Feature Compression

Volume 15, Number 7, July 2019, pp. 1802-1812
DOI: 10.23940/ijpe.19.07.p6.18021812

Ying Wanga, Aili Wanga, Ronghui Wangb, Haiyang Liua, and Yuji Iwahoric

aHigher Education Key Lab for Measuring and Control Technology and Instrumentations of Heilongjiang
Harbin University of Science and Technology, Harbin, 150001, China
bHeilongjiang Province Public Security Department, Harbin, 150001, China
cComputer Science, Chubu University, Aichi, 487-8501, Japan

 

(Submitted on December 8, 2018; Revised on January 11, 2019; Accepted on February 15, 2019)

Abstract:

The main focus of target tracking is robustness and efficiency. Because of challenges such as background clutter, occlusion, and rotation, high robustness and efficiency cannot be achieved simultaneously. A tracking framework of perceptual correlation filter is improved to achieve high-speed computation between real-time trackers. The main contribution to high-speed computing speed comes from improved depth feature compression, which is realized by combining content-aware features with multiple automatic encoders. In the pre-training stage, an automatic encoder is trained for each class separately. In order to obtain the feature map suitable for target tracking, the orthogonal loss function is added to the training stage and the fine-tuning self-encoder stage. Experiments show that the improved algorithm demonstrates great improvement in accuracy and speed.

 

References: 16

  1. D. S. Bolme, J. R. Beveridge, B. A. Draper, and Y. M. Lui, “Visual Object Tracking using Adaptive Correlation Filters,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2544-2550, 2010
  2. J. Choi, H. Chang, J. Jeong, Y. Demiris, and J. Y. Choi, “Visual Tracking using Attention-Modulated Disintegration and Integration,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 4321-4330, 2016
  3. M. Danelljan, G. Häger, F. S. Khan, and M. Felsberg, “Convolutional Features for Correlation Filter based Visual Tracking,” in Proceedings of IEEE International Conference on Computer Vision Workshop, pp. 621-629, 2015
  4. M. Danelljan, G. Häger, F. S. Khan, and M. Felsberg, “Learning Spatially Regularized Correlation Filters for Visual Tracking,” in Proceedings of IEEE International Conference on Computer Vision, pp. 4310-4318, 2015
  5. B. Babenko, M. H. Yang, and S. Belongie, “Visual Tracking with Online Multiple Instance Learning,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 983-990, 2009
  6. B. Babenko, M. H. Yang, and S. Belongie, “Robust Object Tracking with Online Multiple Instance Learning,” IEEE Transactions on Pattern Analysis & Machine Intelligence, Vol. 33, No. 8, pp. 1619-1632, 2011
  7. T. C. Robert, “Mean Shift Blob Tracking through Scale Space,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 234-240, 2003
  8. J. F. Henriques, R. Caseiro, P. Martins, and J. Batista, “High-Speed Tracking with Kernelized Correlation Filters,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 37, No. 3, pp. 583-596, 2015
  9. M. Danelljan, G. Hager, F. S. Khan, and M. Felsberg, “Learning Spatially Regularized Correlation Filters for Visual Tracking,” in Proceedings of IEEE International Conference on Computer Vision, pp. 4310-4318, 2015
  10. E. Park, H. Ju, Y. M. Jeong, and S. Y. Min, “Tracking-Learning-Detection Adopted Unsupervised Learning Algorithm,” in Proceedings of Seventh International Conference on Knowledge and Systems Engineering, pp. 234-237, 2015
  11. G. E. Hinton, S. Osindero, and Y. W. Teh, “A Fast Learning Algorithm for Deep Belief Nets,” Neural Computation, Vol. 18, No. 7, pp. 1527-1554, 2006
  12. J. Zhang, S. Ma, and S. S. MEEM, “Robust Tracking via Multiple Experts using Entropy Minimization,” in Proceedings of European Conference on Computer Vision, pp. 188-203, 2014
  13. Y. Wu, J. Lim, and M. H. Yang, “Online Object Tracking: A Benchmark,” in Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2411-2418, 2013
  14. O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, et al., “Imagenet Large Scale Visual Recognition Challenge,” International Journal of Computer Vision, Vol. 115, No. 3, pp. 211-252, 2015
  15. R. Zhao, W. L. Ouyang, H. S. Li, and X. G. Wang, “Saliency Detection by Multi-Context Deep Learning,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1265-1274, 2015
  16. C. Ma, J. B. Huang, X. K. Yang, and M. H. Yang, “Hierarchical Convolutional Features for Visual Tracking,” in Proceedings of IEEE International Conference on Computer Vision, pp. 3074-3082, 2015

 

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