Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Anti-occlusion particle filter object-tracking method based on feature fusion

  • Author(s): Ruohong Huan 1 ; Shenglin Bao 1 ; Chu Wang 1 ; Yun Pan 2
    • View affiliations
  • Source: Volume 12, Issue 9, September 2018, p. 1529 – 1540
    DOI:  10.1049/iet-ipr.2017.1068 , Print ISSN 1751-9659, Online ISSN 1751-9667
© The Institution of Engineering and Technology
Received 15/11/2017, Accepted 25/03/2018, Revised 15/03/2018, Published 28/03/2018

A new anti-occlusion particle filter object-tracking method based on feature fusion is proposed in this study. Colour and local binary pattern features are extracted and additively fused with a deterministic coefficient, which is calculated based on the difference between the object features and the background. An integral cumulative histogram is proposed to reduce the computational cost of feature extraction. A new occlusion determination method is proposed, and corresponding tracking strategies are also put forward for various occlusion conditions; in the case of partial occlusion, block tracking is carried out, and in the case of serious occlusion, the least-square method is used to predict the object position. Context Aware Vision using Image-based Active Recognition (CAVIAR) and Video Image Retrieval and Analysis Tool (VIRAT) video libraries are used to validate the method. The experimental results show that the proposed method can describe an object effectively and improve tracking stability and robustness under the occlusion conditions.

Inspec keywords: feature extraction; image filtering; particle filtering (numerical methods); least squares approximations; image colour analysis; object tracking

Other keywords: CAVIAR video library; deterministic coefficient; computational cost reduction; least-square method; occlusion determination method; antiocclusion particle filter object-tracking method; VIRAT video library; tracking stability; feature fusion; colour local binary pattern feature extraction; integral cumulative histogram

Subjects: Interpolation and function approximation (numerical analysis); Other topics in statistics; Interpolation and function approximation (numerical analysis); Computer vision and image processing techniques; Image recognition; Other topics in statistics

References

    1. 1)
      • 26. ‘CAVIAR Test Case Scenarios’. Available at http://groups.inf.ed.ac.uk/vision/CAVIAR/CAVIARDATA1/, accessed January 2004.
    2. 2)
      • 7. Lin, S.D., Lin, J.J., Chuang, C.Y.: ‘Particle filter with occlusion handling for visual tracking’, IET Image Process., 2015, 9, (11), pp. 959–968.
    3. 3)
      • 18. Li, W., Yao, J., Dong, T., et al: ‘Object tracking based on fragment template and multi-feature adaptive fusion’. 2015 Eighth Int. Symp. Computational Intelligence and Design (ISCID), Hangzhou, 2015, pp. 481–484.
    4. 4)
      • 14. Ojala, T., Pietikäinen, M., Mäenpää, T.: ‘Multiresolution gray-scale and rotation invariant texture classification with local binary patterns’, IEEE Trans. Pattern Anal. Mach. Intell., 2002, 24, (7), pp. 971–987.
    5. 5)
      • 22. Maggio, E., Cavallaro, A.: ‘Multi-part target representation for color tracking’. IEEE Int. Conf. Image Processing, Genova, Italy, 2005, pp. 729–732.
    6. 6)
      • 19. Li, Z., Gao, S., Nai, K., et al: ‘Robust object tracking based on timed motion history image with multi-feature adaptive fusion’. 2016 12th Int. Conf. Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), Changsha, China, 2016, pp. 845–851.
    7. 7)
      • 15. Song, K.C., Yan, Y.H., Chen, W.H., et al: ‘Research and perspective on local binary pattern’, Acta Autom. Sin., 2013, 39, (6), pp. 730–744.
    8. 8)
      • 17. Gu, X., Wang, H.T., Wang, L.F., et al: ‘Fusing multiple features for object tracking based on uncertainty measurement’, Zidonghua Xuebao/Acta Autom., 2011, 37, pp. 550–559.
    9. 9)
      • 23. Adam, A., Rivlin, E., Shimshoni, I.: ‘Robust fragment based tracking using integral histogram’. 2006 IEEE Computer Society Conf. Computer Vision and Pattern Recognition (CVPR'06), New York, USA, 2006, pp. 798–805.
    10. 10)
      • 27. Oh, S., Hoogs, A., Perera, A., et al: ‘A large-scale benchmark dataset for event recognition in surveillance video’. Proc. IEEE Computer Vision and Pattern Recognition (CVPR), Providence, USA, 2011, pp. 3153–3160.
    11. 11)
      • 24. Fan, G.: ‘The research on target tracking algorithm based on particle filter’ (South China University of Technology, Guangzhou, 2013).
    12. 12)
      • 1. Ding, D., Jiang, Z., Liu, C.: ‘Object tracking algorithm based on particle filter with color and texture feature’. 2016 35th Chinese Control Conf. (CCC), Chengdu, 2016, pp. 4031–4036.
    13. 13)
      • 9. Lin, D.T., Chang, Y.H.: ‘Occlusion handling for pedestrian tracking using partial object template-based component particle filter’, IADIS Int. J. Comput. Sci. Inf Syst., 2013, 8, (2), pp. 40–50.
    14. 14)
      • 25. Montemayor, A.S., Pantrigo, J.J., Hernández, J.: ‘A memory-based particle filter for visual tracking through occlusions’. Proc. Int. Work-Conf. Interplay between Natural and Artificial Computation, Part II (IWINAC ‘09), Berlin, Germany, 2009, vol. 5602, pp. 274–283.
    15. 15)
      • 20. Ying, H., Qiu, X., Song, J., et al: ‘Particle filtering object tracking based on texture and color’. 2010 Int. Symp. Intelligence Information Processing and Trusted Computing, Huanggang, China, 2010, pp. 626–630.
    16. 16)
      • 10. Dou, J.J., Wen, J., Liu, C.Q: ‘Histogram-based color image retrieval’, Infrared Laser Eng., 2005, 34, (1), pp. 84–88.
    17. 17)
      • 16. Porikli, F.: ‘Integral histogram: a fast way to extract histograms in Cartesian spaces’. IEEE Computer Society Conf. Computer Vision and Pattern Recognition (CVPR'05), San Diego, USA, 2005, pp. 829–836.
    18. 18)
      • 8. Meshgi, K., Maeda, S., Oba, S., et al: ‘An occlusion-aware particle filter tracker to handle complex and persistent occlusions’, Comput. Vis. Image Underst., 2016, 150, pp. 81–94.
    19. 19)
      • 11. Bhattacharyya, A.: ‘On a measure of divergence between two multinomial populations’, Sankhyā: Indian J. Stat. (1933–1960), 1946, 7, (4), pp. 401–406.
    20. 20)
      • 21. Yang, J., Lu, X., Lu, H., et al: ‘An improved particle filter algorithm for target tracking’. 2012 IEEE Int. Conf. Intelligent Control, Automatic Detection and High-End Equipment, Beijing, China, 2012, pp. 103–107.
    21. 21)
      • 13. Papadakis, N., Provenzi, E., Caselles, V.: ‘A variational model for histogram transfer of color images’, IEEE Trans. Image Process., 2011, 20, (6), pp. 1682–1695.
    22. 22)
      • 4. Zhang, J., Lu, H., Lu, X., et al: ‘Partial occlusion tracking with blocks and discriminative model’. 2014 Ninth IEEE Conf. Industrial Electronics and Applications (ICIEA 2014), Hangzhou, China, 2014, pp. 877–882.
    23. 23)
      • 12. Huang, A.Q., Hou, Z.Q., Yu, W.S., et al: ‘A visual object tracking method based on improved Bhattacharyya coefficient and model update strategy’. 2014 Fifth Int. Conf. Intelligent Systems Design and Engineering Applications, Hunan, 2014, pp. 100–105.
    24. 24)
      • 6. Liu, J.W., Sun, W.P., Xia, T.: ‘Adaptive structured sub-blocks tracking’, Neurocomputing, 2016, 204, pp. 97–105.
    25. 25)
      • 2. Yan, Y., Wang, J., Li, C., et al: ‘Object tracking using SIFT features in a particle filter’. 2011 IEEE Third Int. Conf. Communication Software and Networks (ICCSN 2011), Xi'an, China, 2011, pp. 384–388.
    26. 26)
      • 3. Scharcanski, J., De Oliveira, A.B., Cavalcanti, P.G., et al: ‘A particle-filtering approach for vehicular tracking adaptive to occlusions’, IEEE Trans. Veh. Technol., 2011, 60, (2), pp. 381–389.
    27. 27)
      • 5. Qi, Y., Wang, Y.: ‘Memory-based state estimation for handling occlusion during object tracking by particle filter’. Int. Conf. Information Science and Technology (ICIST 2011), Nanjing, China, 2011, pp. 953–957.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2017.1068
Loading

Related content

content/journals/10.1049/iet-ipr.2017.1068
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address