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Multiple features fusion based video face tracking

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Abstract

With the development of monitoring equipment and artificial intelligence technology, video face tracking under the big data background has become an important research hot spot in the field of public security. In order to track robustly under the circumstances of illumination variation, background clutter, fast motion, partial occlusion and so on, this paper proposed an algorithm combining a multi-feature fusion in the frame of particle filter and an improved mechanism, which consists of three main steps. At first, the color and edge features of human face were extracted from the video sequence. Meanwhile, color histograms and edge orientation histograms (EOH) were used to describe the facial features and beneficial to improve the efficiency of calculation. Then we employed a self-adaptive features fusion strategy to calculate the particle weight, which can effectively enhance the reliability of face tracking. Moreover, in order to solve the computational efficiency problem caused by too many particles, we added the integral histogram method to simplify the calculation complexity. At last, the object model was updated between the current object model and the initial model for alleviating the model drifts. Experiments conducted on testing dataset show that this proposed approach can robustly track single face with the cases of complex backgrounds, such as similar skin color, illumination change and occlusion, and perform better than color-based and edge-based methods in terms of both quantitative metrics and visual quality.

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References

  1. Arulampalam MS, Maskell S, Gordon N, Clapp T (2002) A tutorial on particle filters for on-line non-linear/non-Gaussian Bayesian tracking. IEEE Trans Sign Proc 50(2):174–188

    Article  Google Scholar 

  2. Bos R, De Waele S, Broersen PMT (2002) Autoregressive spectral estimation by application of the burg algorithm to irregularly sampled data. IEEE Trans Instrum Meas 51(6):1289–1294

    Article  Google Scholar 

  3. Comaniciu D, Ramesh V, Meer P (2003) Kernel-based object tracking. IEEE Trans Patt Anal Mach Intel 25(5):564–575

    Article  Google Scholar 

  4. Dou J, Li J, Zhang Z, Han S (2012) Face tracking with an adaptive Adaboost-based particle filter. 24th IEEE Chin Control Dec Conf (CCDC):3626–3631

  5. Gustafsson F, Gunnarsson F, Bergman N, Forssell U, Jansson J, Karlsson R, Nordlund PJ (2002) Particle filters for positioning, navigation, and tracking. IEEE Trans Signal Process 50(2):425–437

    Article  Google Scholar 

  6. Jiang X, Yu H, Lu Y, Liu H (2016) A fusion method for robust face tracking. Multimed Tools Appl 75(19):11801–11813

    Article  Google Scholar 

  7. Kailath T (1967) The Divergence and Bhattacharyya Distance Measures in Signal Selection. IEEE Trans Commun Technol 15(1):52–60

    Article  Google Scholar 

  8. Lee J, Park G-L, Kwak H-Y (2011) Two-directional two-dimensional random projection and its variations for face and palmprint recognition. ICCSA. Int Conf 32(10):707–727

    Google Scholar 

  9. Leng L, Li M, Leng L, Teoh ABJ (2013) Conjugate 2DPalmHash code for secure palm-print-vein verification. 2013 6th Int Cong Image Sign Proc (CISP) 70(1):495–523

    Google Scholar 

  10. Leng L, Li M, Kim C, Bi X (2015) Dual-source discrimination power analysis for multi-instance contactlesLeng, L., Li, M., Leng, L., & Teoh, A. B. J. (2013). Conjugate 2DPalmHash code for secure palm-print-vein verification. 2013 6th Int Cong Image Sign Proc (CISP) 70(1):495–523

    Google Scholar 

  11. Li X, Dick A, Shen C, Zhang Z, Hengel AVD, Wang H (2013) Visual tracking with spatio-temporal Dempster-Shafer information fusion. IEEE Trans Image Proc 22(8):3028–3040

    Article  MathSciNet  MATH  Google Scholar 

  12. Li Y, Wang G, Nie L, Wang Q (2018) Distance metric optimization driven convolutional neural network for age invariant face recognition. Pattern Recogn 75:51–62

    Article  Google Scholar 

  13. Lin Y, Shen J, Cheng S, Pantic M (2018) Mobile Face Tracking: A Survey and Benchmark. In: Proceedings of Computer Vision and Pattern Recognition (CVPR), arXiv:1805.09749

  14. Liu Y, Nie L, Liu L, Rosenblum DS (2016) From action to activity: sensor-based activity recognition. Neurocomputing 181:108–115

    Article  Google Scholar 

  15. Liu Y, Zheng Y, Liang Y, Liu S, David S (2016) UrbanWater quality prediction based on multi-task multi-view. Learning. 34(4):44–48

    Google Scholar 

  16. Lu L, Zhang J, Xu J, Khan MK, Alghathbar K (2010) Dynamic weighted discrimination power analysis in DCT domain for face and palmprint recognition. 2010. Int Conf Inform Commun Technol Conver (ICTC) 34(4):44–48

    Google Scholar 

  17. Sadeghian A, Alahi A, Savarese S (2017) Tracking The Untrackable: Learning To Track Multiple Cues with Long-Term Dependencies. In: Proceedings of Computer Vision and Pattern Recognition (CVPR), arXiv:1701.01909

  18. SOBEL I (1990) An Isotropic 3×3 Gradient Operator, Machine Vision for Three – Dimensional Scenes, Freeman, H., Academic Press, 376–379

  19. Swain MJ, Ballard DH (1991) Color indexing. Int J Comput Vis 7(1):11–32

    Article  Google Scholar 

  20. Visual Tracker Benchmark. http://cvlab.hanyang.ac.kr/tracker_benchmark/datasets.html

  21. Wang J, Yagi Y (2008) Integrating color and shape-texture features for adaptive real-time object tracking. IEEE Trans Image Process 17(2):235–240

    Article  Google Scholar 

  22. Wang J, Jiang YX, Tang CH (2012) Face tracking based on particle filter using color histogram and contour distributions. Poto-Electron Eng 39(10):32–39

    Google Scholar 

  23. Wang L, Yan H, Lv K, Pan C (2014) Visual Tracking Via Kernel Sparse Representation With Multikernel Fusion. IEEE Trans Circ Syst Video Technol 24(7):1132–1141

    Article  Google Scholar 

  24. Welch G, Bishop G (2017) An introduction to the Kalman filter. Univ North Carol Chapel Hill 8(7):127–132

    Google Scholar 

  25. Yang D, Zhang Y, Ji R, Li Y, Huangfu L, Yang Y (2015) An improved spatial histogram and particle filter face tracking. Genetic and evolutionary computing, advances in intelligent systems. Computing 329:257–267

    Google Scholar 

  26. Yoon JH, Yang MH, Lim J, Yoon KJ (2015) Bayesian Multi-object Tracking Using Motion Context from Multiple Objects. In: IEEE Winter Conference on Applications of Computer Vision (WACV), pp.33–40

  27. Zhu W, Levinson S (2000) Edge Orientation-Based Multi-View Object Recognition. Proc 15th Int Conf Patt Recog (ICPR):936–939

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Acknowledgements

This work was supported in part by NSFC (61572286 and 61472220), NSFC Joint with Zhejiang Integration of Informatization and Industrializaiton under Key Project (U1609218), and the Fostering Project of Dominant Discipline a Talent Team of Shandong Province Higher Education.

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Authors and Affiliations

  1. Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, Jinan, 250014, Shandong, China

    Tianping Li

  2. Department of Physics and Electronics, Shandong Normal University, Jinan, 250014, Shandong, China

    Tianping Li

  3. Yancheng Biological Engineering Higher Vocational Technology School, Yancheng, 224051, Jiangsu, China

    Pingping Zhou

  4. Department of Computer Science & Technology, Shandong University of Finance and Economics, Jinan, 250014, Shandong, China

    Hui Liu

  5. Medical Physics Division in the Department of Radiation Oncology, Stanford University, Palo Alto, CA, 94305, USA

    Hui Liu

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  1. Tianping Li
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  2. Pingping Zhou
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  3. Hui Liu
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Correspondence to Hui Liu.

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Li, T., Zhou, P. & Liu, H. Multiple features fusion based video face tracking. Multimed Tools Appl 78, 21963–21980 (2019). https://doi.org/10.1007/s11042-019-7414-x

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