DOI QR코드

DOI QR Code

Convolutional Neural Network with Particle Filter Approach for Visual Tracking

  • Received : 2017.07.04
  • Accepted : 2017.08.11
  • Published : 2018.02.28

Abstract

In this paper, we propose a compact Convolutional Neural Network (CNN)-based tracker in conjunction with a particle filter architecture, in which the CNN model operates as an accurate candidates estimator, while the particle filter predicts the target motion dynamics, lowering the overall number of calculations and refines the resulting target bounding box. Experiments were conducted on the Online Object Tracking Benchmark (OTB) [34] dataset and comparison analysis in respect to other state-of-art has been performed based on accuracy and precision, indicating that the proposed algorithm outperforms all state-of-the-art trackers included in the OTB dataset, specifically, TLD [16], MIL [1], SCM [36] and ASLA [15]. Also, a comprehensive speed performance analysis showed average frames per second (FPS) among the top-10 trackers from the OTB dataset [34].

Keywords

References

  1. B. Babenko, M.H. Yang and S. Belongie, "Robust object tracking with online multiple instance learning," IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(8), 1619-1632, 2011. https://doi.org/10.1109/TPAMI.2010.226
  2. J. S. Bae and T. L. Song, "Image tracking algorithm using template matching and psnf-m," International Journal of Control Automation and Systems, 6(3), p. 413, 2008.
  3. R. R. Cabrera, T. Tuytelaars and L. Van Gool, "Efficient multi-camera detection, tracking, and identi_cation using a shared set of haar-features," in Proc. of Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pp. 65-71, 2011.
  4. K. Chat_eld, K. Simonyan, A. Vedaldi and A. Zisserman, "Return of the devil in the details: Delving deep into convolutional nets," in Proc. of British Machine Vision Conference, 2014.
  5. D. Ciregan, U. Meier and J. Schmidhuber, "Multi-column deep neural networks for image classification," in Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on (2012) pp. 3642-3649.
  6. A. Doucet, N. De Freitas and N. Gordon, "An introduction to sequential monte carlo methods," in Sequential Monte Carlo methods in practice, pp. 3-14, Springer, 2011.
  7. J. Fan, W. Xu, Y.Wu and Y. Gong, "Human tracking using convolutional neural networks," IEEE Transactions on Neural Networks, 21(10), 1610-1623, 2010. https://doi.org/10.1109/TNN.2010.2066286
  8. R. Girshick, "Fast R-CNN," in Proc. of the IEEE International Conference on Computer Vision, pp. 1440-1448, 2015.
  9. R. Girshick, J. Donahue, T. Darrell and J. Malik, "Rich feature hierarchies for accurate object detection and semantic segmentation," in Proc. of the IEEE conference on computer vision and pattern recognition, pp. 580-587, 2014.
  10. H. Grabner, M. Grabner and H. Bischof, "Real-time tracking via on-line boosting," in BMVC, Vol. 1(5), p. 6, 2006.
  11. S. Hare, A. Sa_ari and P. H. Torr, "Struck: Structured output tracking with kernels," in Proc. of 2011 International Conference on Computer Vision, pp. 263-270, 2011.
  12. J. F. Henriques, R. Caseiro, P. Martins and J. Batista, "Exploiting the circulant structure of tracking-by-detection with kernels," in Proc. of European conference on computer vision (2012), pp. 702-715, August 27, 2016.
  13. 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, 37(3), 583-596, 2015. https://doi.org/10.1109/TPAMI.2014.2345390
  14. S. Hong, T. You, S. Kwak and B. Han, "Online tracking by learning discriminative saliency map with convolutional neural network," arXiv preprint arXiv:1502.06796, 2015.
  15. X. Jia, H. Lu and M.-H. Yang, "Visual tracking via adaptive structural local sparse appearance model," in Proc. of Computer vision and pattern recognition (CVPR), 2012 IEEE Conference on, pp. 1822-1829, 2012.
  16. Z. Kalal, K. Mikolajczyk and J. Matas, "Tracking-learning-detection," IEEE transactions on pattern analysis and machine intelligence, 34(7), 1409-1422, 2012. https://doi.org/10.1109/TPAMI.2011.239
  17. M. Kristan, J. Matas, A. Leonardis, M. Felsberg, L. Cehovin, G. Fernandez, T. Vojir, G. Hager, G. Nebehay and R. Pugfelder, "The visual object tracking VOT2015 challenge results," in Proc. of the IEEE International Conference on Computer Vision Workshops, pp. 1-23, 2015.
  18. A. Krizhevsky, I. Sutskever and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," Communications of the ACM, pp. 84-90, 2017.
  19. J. Kwon and K. M. Lee, "Visual tracking decomposition," in Proc. of Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pp. 1269-1276, 2010.
  20. J. Kwon and K. M. Lee, "Tracking by sampling trackers," in Proc. of 2011 International Conference on Computer Vision, pp. 1195-1202, 2011.
  21. H. Li, Y. Li and F. Porikli, "Deeptrack: Learning discriminative feature representations online for robust visual tracking," IEEE Transactions on Image Processing, 25(4), 1834-1848, 2016. https://doi.org/10.1109/TIP.2015.2510583
  22. J. Long, E. Shelhamer and T. Darrell, "Fully convolutional networks for semantic segmentation," in Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431-3440, 2015.
  23. C. Ma, J.-B. Huang, X. Yang and M.H. Yang, "Hierarchical convolutional features for visual tracking," in Proc. of the IEEE International Conference on Computer Vision, pp. 3074-3082, 2015.
  24. H. Nam and B. Han, "Learning multi-domain convolutional neural networks for visual tracking," arXiv preprint arXiv:1510.07945 (2015).
  25. S. S. Nejhum, J. Ho and M.-H. Yang, "Visual tracking with histograms and articulating blocks, in Computer Vision and Pattern Recognition," Proc. of CVPR 2008 IEEE Conference on, pp. 1-8, 2008.
  26. N. Qian, "On the momentum term in gradient descent learning algorithms," Neural networks, 12(1), 145-151, 1999. https://doi.org/10.1016/S0893-6080(98)00116-6
  27. S. Ren, K. He, R. Girshick and J. Sun, "Faster R-CNN: Towards real-time object detection with region proposal networks," Advances in neural information processing systems (2015), pp. 91-99. August 27, 2016.
  28. L. Sevilla-Lara and E. Learned-Miller, "Distribution fields for tracking," in Proc. of Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pp. 1910-1917, 2012.
  29. K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014.
  30. J. J. Tompson, A. Jain, Y. LeCun and C. Bregler, "Joint training of a convolutional network and a graphical model for human pose estimation," Advances in neural information processing systems, pp. 1799-1807, 2014.
  31. A. Toshev and C. Szegedy, "Deeppose: Human pose estimation via deep neural networks," in Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1653-1660, 2014.
  32. A. Vedaldi and K. Lenc, "Matconvnet: Convolutional neural networks for MATLAB," in Proc. of the 23rd ACM international conference on Multimedia, pp. 689-692, 2015.
  33. L. Wang, "Video image object tracking algorithm based on improved principal component analysis," Journal of Multimedia, 9(5), 722-728, 2014.
  34. Y. Wu, J. Lim and M.H. Yang, "Online object tracking: A benchmark," in CVPR (IEEE Computer Society), pp. 2411-2418, 2013.
  35. K. Zhang, Q. Liu, Y. Wu, and M.-H. Yang, "Robust Visual Tracking via Convolutional Networks without Training," IEEE Transactions on Image Processing, pp. 1-1, 2016.
  36. W. Zhong, H. Lu and M.H. Yang, "Robust object tracking via sparsity-based collaborative model," in Proc. of Computer vision and pattern recognition (CVPR), 2012 IEEE Conference on, pp. 1838-1845, 2012.

Cited by

  1. An Intelligent Automatic Human Detection and Tracking System Based on Weighted Resampling Particle Filtering vol.4, pp.4, 2018, https://doi.org/10.3390/bdcc4040027