Accurate human tracking in surveillance scenes is one of the preliminary requirements for other tasks. However, when the human target is small, the extracted features may not be prominent and thus the tracking performance is unsatisfactory. The colour feature is relatively robust to the change of target size and shape, but it is prone to be affected by the background information. For the above reasons, the authors introduce random walker segmentation into human tracking and determine the background region according to the distribution characteristics of segmentation results. Even if the colour of the target is very similar to that of the background, this algorithm can segment the target. Furthermore, the principal component analysis method is used to distinguish human targets from the background as well. During tracking, the authors prevent the degradation of the target model by adding new target information. In order to overcome the mean-shift local optimisation problem, the authors search for the candidate target region with the largest weight according to the sum of all probability in each region. Experimental results further show that the authors’ tracking algorithm demonstrates better performance on tracking small human target under some challenging scenes compared with several existing tracking methods.

References

1. 1)
  - 6. Rao, G.M., Satyanarayana, C.: ‘Visual object target tracking using particle filter: a survey’, Int. J. Image Graph., 2013, 5, (6), pp. 57–71.
2. 2)
  - 9. Wang, Y., Tang, X., Cui, Q.: ‘Dynamic appearance model for particle filter based visual tracking’, Pattern Recogn., 2012, 45, (12), pp. 4510–4523.
3. 3)
  - 23. Cai, Z., Wen, L., Lei, Z., et al: ‘Robust deformable and occluded object tracking with dynamic graph’, IEEE Trans. Image Process., 2014, 23, (12), pp. 5497–5509.
4. 4)
  - 13. Yang, F., Lu, H., Chen, Y.W.: ‘Human tracking by multiple Kernel boosting with locality affinity constraints’, Lect. Notes Comput. Sci., 2011, 6495, pp. 39–50.
5. 5)
  - 24. Zhang, K., Zhang, L., Yang, M.H.: ‘Fast compressive tracking’, IEEE Trans. Pattern. Anal. Mach. Intell., 2014, 36, (10), pp. 2002–2015.
6. 6)
  - 18. Biggs, N.: ‘Algebraic potential theory on graphs’, B. Lond. Math. Soc., 1997, 29, (6), pp. 641–682.
7. 7)
  - 17. Grady, L.: ‘Random walks for image segmentation’, IEEE Trans. Pattern. Anal. Mach. Intell., 2006, 28, (11), pp. 1768–1783.
8. 8)
  - 26. Li, Y., Zhu, J.: ‘A scale adaptive Kernel correlation filter tracker with feature integration’ (Springer International Publishing, 2014), pp. 254–265.
9. 9)
  - 10. Zhang, P., Zhang, Y., Thomas, T., et al: ‘Moving people tracking with detection by latent semantic analysis for visual surveillance applications’, Multimed. Tools Appl., 2014, 68, (3), pp. 991–1021.
10. 10)
  - 19. Doyle, P.G., Snell, J.L.: ‘Random walks and electric networks’, Am. Math. Mon., 2000, 26, (4), pp. 595–599.
11. 11)
  - 5. Isard, M., Blake, A.: ‘Condensation—conditional density propagation for visual tracking’, Int. J. Comput. Vis., 1998, 29, (1), pp. 5–28.
12. 12)
  - 25. Henriques, J.O.F., Caseiro, R., Martins, P., et al: ‘High-speed tracking with kernelized correlation filters’, IEEE Trans. Pattern. Anal. Mach. Intell., 2014, 37, (3), pp. 583–596.
13. 13)
  - 22. Bradski, G.R.: ‘Computer vision face tracking for use in a perceptual user interface’, IEEE Trans. Aero. Elec. Sys, 1998, q2, pp. 214–219.
14. 14)
  - 1. Ning, J., Zhang, L., Zhang, D., et al: ‘Robust mean-shift tracking with corrected background-weighted histogram’, IET Comput. Vis., 2012, 6, (1), pp. 62–69.
15. 15)
  - 8. Raziperchikolaei, R., Jamzad, M.: ‘Visual tracking using D2-clustering and particle filter’. IEEE Int. Symp. Signal Processing, Ho Chi Minh City, Vietnam, December 2012, pp. 230–235.
16. 16)
  - 2. Fang, J., Yang, J., Liu, H.: ‘Efficient and robust fragments-based multiple Kernels tracking’, AEU-Int. J. Electron. C., 2011, 65, (11), pp. 915–923.
17. 17)
  - 14. Mei, X., Ling, H.: ‘Robust visual tracking using L1 minimization’. Proc. Int. Conf. Computer Vision, Kyoto, Japan, September 2009, pp. 1436–1443.
18. 18)
  - 15. Jia, X., Lu, H., Yang, M.-H.: ‘Visual tracking via adaptive structural local sparse appearance model’. Proc. IEEE Conf. Computer Vision and Pattern Recognition, Providence, RI, June 2012, pp. 1822–1829.
19. 19)
  - 20. Shi, J., Malik, J.: ‘Normalized cuts and image segmentation’, IEEE Trans. Pattern Anal., 2000, 22, (8), pp. 888–905.
20. 20)
  - 12. Yang, B., Nevatia, R.: ‘Online learned discriminative part-based appearance models for multi-human tracking’. Proc. European Conf. Computer Vision, 2012, pp. 484–498.
21. 21)
  - 27. Viola, P., Jones, M.: ‘Rapid object detection using a boosted cascade of simple features’. Proc. of the 2001 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, 2001. CVPR 2001.2001, vol. 511, pp. I-511–I-518.
22. 22)
  - 7. Liu, H., Sun, F.: ‘Efficient visual tracking using particle filter with incremental likelihood calculation’, Inf. Sci., 2012, 195, (13), pp. 141–153.
23. 23)
  - 11. Zhang, H., Zhan, J., Su, Z., et al: ‘Online human tracking via superpixel-based collaborative appearance model’. IEEE Int. Conf. Multi., Chengdu, China, July 2014, pp. 1–6.
24. 24)
  - 4. Comaniciu, D., Ramesh, V., Meer, P.: ‘Kernel-based object tracking’, IEEE Trans. Pattern. Anal. Mach. Intell., 2003, 25, (5), pp. 564–577.
25. 25)
  - 21. Babenko, B., Yang, M.H., Belongie, S.: ‘Robust object tracking with online multiple instance learning’, IEEE Trans. Pattern. Anal. Mach. Intell., 2011, 33, (8), pp. 1619–1632.
26. 26)
  - 16. Kalal, Z., Mikolajczyk, K., Matas, J.: ‘Tracking-learning-detection’, IEEE Trans. Pattern. Anal. Mach. Intell., 2012, 34, (7), pp. 1409–1422.
27. 27)
  - 3. An, X., Kim, J., Han, Y.: ‘Optimal colour-based mean shift algorithm for tracking objects’, IET Comput. Vis., 2014, 8, (3), pp. 235–244.

Random walks colour histogram modification for human tracking

References

Related content