DOI QR코드

DOI QR Code

Directional Particle Filter Using Online Threshold Adaptation for Vehicle Tracking

  • Received : 2017.03.19
  • Accepted : 2017.09.15
  • Published : 2018.02.28

Abstract

This paper presents an extended particle filter to increase the accuracy and decrease the computation load of vehicle tracking. Particle filter has been the subject of extensive interest in video-based tracking which is capable of solving nonlinear and non-Gaussian problems. However, there still exist problems such as preventing unnecessary particle consumption, reducing the computational burden, and increasing the accuracy. We aim to increase the accuracy without an increase in computation load. In proposed method, we calculate the direction angle of the target vehicle. The angular difference between the direction of the target vehicle and each particle of the particle filter is observed. Particles are filtered and weighted, based on their angular difference. Particles with angular difference greater than a threshold is eliminated and the remaining are stored with greater weights in order to increase their probability for state estimation. Threshold value is very critical for performance. Thus, instead of having a constant threshold value, proposed algorithm updates it online. The first advantage of our algorithm is that it prevents the system from failures caused by insufficient amount of particles. Second advantage is to reduce the risk of using unnecessary number of particles in tracking which causes computation load. Proposed algorithm is compared against camshift, direction-based particle filter and condensation algorithms. Results show that the proposed algorithm outperforms the other methods in terms of accuracy, tracking duration and particle consumption.

Keywords

References

  1. M. E. Yildirim, J. K. Song, J. S. Park, B. W. Yoon, Y. S. Yu, "Robust Vehicle Tracking with multi-feature particle filter," in Proc. of Int. Conf. on Multimedia, Computer Graphics and Broadcasting, pp. 191-196, December 8-10, 2011.
  2. P.G.V. Naranjo, M. Shojafar, H. Mostafaei, "P-SEP: A prolong stable election routing algorithm for energy-limited heterogeneous fog-supported wireless sensor networks," Journal of Supercomputing, vol. 73, no. 2, pp. 733-755, February, 2017. https://doi.org/10.1007/s11227-016-1785-9
  3. S. Shamshirband, M. Shojafar, A.R. Hosseinabadi, A. Abraham, "OVRP_ICA: An imperialist-based optimization algorithm for the open vehicle routing problem, " in Proc. of Int. Conf. on Hybrid Artificial Intelligence Systems, pp. 221-233, June 21-23, 2015.
  4. M. A. Qureshi, R. M. Noor, S. Shamshirband, S. Parveen, M. Shiraz, A. Gani, "A survey on obstacle modeling patterns in radio propagation models for vehicular ad hoc networks," Arabian Journal for Science and Engineering, vol. 40, no. 5, pp. 1385-1407, February, 2015. https://doi.org/10.1007/s13369-015-1600-6
  5. R. Kodama, M. Koge, S. Taguchi and H. Kajimoto, "COMS-VR: Mobile virtual reality entertainment system using electric car and head-mounted display," in Proc. of IEEE Symposium on 3D User Interfaces (3DUI), pp. 130-133, March 18-19, 2017.
  6. E. Baccarelli, P. G. Vinueza Naranjo, M. Scarpiniti, M. Shojafar, J. H. Abawajy, "Fog of Everything: Energy-efficient Networked Computing Architectures, Research Challenges, and a Case Study, " IEEE Access ,vol. 5, pp. 9882-9910, May, 2017. https://doi.org/10.1109/ACCESS.2017.2702013
  7. M. Shojafar, N. Cordeschi, E. Baccarelli, "Energy-efficient Adaptive Resource Management for Real-time Vehicular Cloud Services," IEEE Transactions on Cloud Computing, vol. PP, no.99, pp.1-1, April, 2016.
  8. Z.L. Szpak and J.R. Tapamo, "Maritime surveillance: Tracking ships inside a dynamic background using a fast level-set," Expert Systems with Applications, vol. 38, no. 9, pp. 6669-6680, June, 2011. https://doi.org/10.1016/j.eswa.2010.11.068
  9. Y.H. Ma, Z.H. Mao, W.Y. Jia, C.L. Li, J.W. Yang, M.G. Sun, "Magnetic hand tracking for human-computer interface," IEEE Transactions on Magnetics, vol. 47, no. 5, pp. 970-973, May, 2011. https://doi.org/10.1109/TMAG.2010.2076401
  10. V.A. Prisacariu and I. Reid, "3D hand tracking for human computer interaction," Image and Vision Computing, vol. 30, no. 3, pp. 236-250, March, 2012. https://doi.org/10.1016/j.imavis.2012.01.003
  11. W. Li, P. Wang, H. Qiao, "Top-down visual attention integrated particle filter for robust object tracking," Signal Processing- Image, vol. 43, pp.28-41, April, 2016. https://doi.org/10.1016/j.image.2016.01.001
  12. C. M. Huang and L.C. Fu, "Multitarget visual tracking based effective surveillance with cooperation of multiple active cameras," IEEE Transactions on Systems, Man and Cybernetics, vol. 41, no. 1, pp. 234-247, February, 2011. https://doi.org/10.1109/TSMCB.2010.2050878
  13. I. Elafi, M. Jedra, N. Zahid, "Unsupervised detection and tracking of moving objects for video surveillance applications," Pattern Recognition Letters, vol. 84, pp.70-77, December, 2016. https://doi.org/10.1016/j.patrec.2016.08.008
  14. D. Y. Kim and H. Jeon, "Data fusion of radar and image measurements for multi-object tracking via Kalman filtering," Information Sciences, vol. 278, pp. 641-652, September, 2014. https://doi.org/10.1016/j.ins.2014.03.080
  15. J. Cesic, I. Markovic, I. Cvisic, I. Petrovic, "Radar and stereo vision fusion for multitarget tracking on the special Euclidean group," Robotics and Autonomous Systems, vol. 83, pp.338-348, September, 2016. https://doi.org/10.1016/j.robot.2016.05.001
  16. A. Xydes, M. Moline, C. G. Lowe, T. J. Farrugia, C. Clark, "Behavioral characterization and Particle Filter localization to improve temporal resolution and accuracy while tracking acoustically tagged fishes," Ocean Engineering, vol. 61, pp. 1-11, March, 2013. https://doi.org/10.1016/j.oceaneng.2012.12.028
  17. J. F. Dou and J. X. Li, "Robust visual tracking based on adaptively multi-feature fusion and particle filter," Optik, vol. 125, no. 5, pp.1680-1686, March, 2014. https://doi.org/10.1016/j.ijleo.2013.10.007
  18. A. Ibarguren, I. Maurtua, M. A. Perez, B. Sierra, "Multiple target tracking based on particle filter for safety in industrial robotic cells," Robotics and Autonomous Systems, vol. 72, pp.105-113, October, 2015. https://doi.org/10.1016/j.robot.2015.05.004
  19. K. D. Sharma, A. Chatterjee, A. Rakshit, "A PSO - Lyapunov hybrid stable adaptive fuzzy tracking control approach for vision-based robot navigation," IEEE Transactions on Instrumentation and Measurement, vol. 61, no. 7, pp.1908-1914, July, 2012. https://doi.org/10.1109/TIM.2012.2182868
  20. T. Li, M. Bolic, P. M. Djuric, "Resampling methods for particle filtering: classification, implementation, and strategies," IEEE Signal Processing Magazine, vol. 32, no. 3, pp. 70-86, May, 2015. https://doi.org/10.1109/MSP.2014.2330626
  21. N. C. Mithun, T. Howlader, S.M.M. Rahman, "Video-based tracking of vehicles using multiple time-spatial images," Expert Systems with Applications, vol. 62, pp.17-31, November, 2016. https://doi.org/10.1016/j.eswa.2016.06.020
  22. I. Majeed and O. Arif, "Non-linear eigenspace visual object tracking," Engineering Applications of Artificial Intelligence, vol. 55, pp. 363-374, October, 2016. https://doi.org/10.1016/j.engappai.2016.08.005
  23. N. Zhao, Y. Xia, C. Xu, X. Shi, Y. Liu, "APPOS: An adaptive partial occlusion segmentation method for multiple vehicles tracking," Journal of Visual Communication and Image Representation, vol. 37, pp. 25-31, May, 2016. https://doi.org/10.1016/j.jvcir.2015.04.011
  24. X. Fei and K.Hashimoto, "An object-tracking algorithm based on particle filtering with region-based level set method," in Proc. of Int. Conf. on Intelligent Robots and Systems, pp.2908-2913, October 18-22, 2010.
  25. B. Tian, Y. Li, B. Li, D. Wen, "Rear-view vehicle detection and tracking by combining multiple parts for complex urban surveillance," IEEE Transactions on Intelligent Transportation Systems, vol. 15, no. 2, pp. 597-606, April, 2014. https://doi.org/10.1109/TITS.2013.2283302
  26. N. J. Gordon, D. J. Salmond, A. F. M. Smith, "Novel approach to nonlinear and non-Gaussian Bayesian state estimation," Radar and Signal Processing, vol.140, no.2, pp.107-113, May 1993. https://doi.org/10.1049/ip-f-2.1993.0015
  27. C. L. Dong and Y. N. Dong, "Survey on video based vehicle detection and tracking algorithms," Journal of Nanjing University of Posts and Telecommunications: Natural Sciences, vol. 29, no. 2, pp. 88-94, April, 2009.
  28. G. S. Walia and R. Kapoor, "Human detection in video and images a-state-of-art survey," International Journal of Pattern Recognition and Artificial Intelligence, vol.28, no. 3, pp.1 -25, May, 2014.
  29. S. J. Hong, M. Bolic, P. M. Djuric, "An efficient fixed-point implementation of residual resampling scheme for high-speed particle filters," IEEE Signal Processing Letters, vol. 11, pp. 482-485, June, 2004. https://doi.org/10.1109/LSP.2004.826634
  30. H. Zhou, Z. Deng, Y. Xia, M. Fu, "A new sampling method in particle filter based on Pearson correlation coefficient," Neurocomputing, vol. 216, pp. 205-215, December, 2016.
  31. Z. Fan, H. Ji, Y. Zhang, "Iterative particle filter for visual tracking," Signal Processing- Image, vol. 36, pp.140-153, August, 2015. https://doi.org/10.1016/j.image.2015.07.001
  32. M. C. Ho, C. C. Chiang, Y. Y. Su, "Object tracking by exploiting adaptive region-wise linear subspace representations and adaptive templates in an iterative particle filter," Pattern Recognition Letters, vol. 33, pp.500-512, April 2012. https://doi.org/10.1016/j.patrec.2011.11.019
  33. W. Qi, X. Zhang, L. Chao, O. Yuanxin, S. Hao, "A robust approach for multiple vehicles tracking using layered particle filter," International Journal of Electronics and Communications, vol. 65, pp. 609-618, July, 2011. https://doi.org/10.1016/j.aeue.2010.06.006
  34. C. Shan, T. Tan, Y.Wei, "Real-time hand tracking using a mean shift embedded particle filter," Pattern Recognition, vol. 40, pp.1958-1970, July, 2007. https://doi.org/10.1016/j.patcog.2006.12.012
  35. H. Liu and F. Sun, "Efficient visual tracking using particle filter with incremental likelihood calculations," Information Sciences, vol.195, pp.141-153, July, 2012. https://doi.org/10.1016/j.ins.2012.01.033
  36. H. A. A. El-Halym, I. I. Mahmoud, S. E. D. Habib, "Proposed hardware architectures of particle for object tracking," EURASIP Journal on Advances in Signal Processing, vol.1, pp.1-19, January, 2012.
  37. M. L. Gao, L. L. Li, X. M. Sun, L. J. Yin, H. T. Li, D. S. Luo, "Firefly algorithm (FA) based particle filter method for visual tracking," Optik, vol. 126, pp. 1705-1711, September, 2015. https://doi.org/10.1016/j.ijleo.2015.05.028
  38. D. Comaniciu, V. Ramesh, P. Meer, "Kernel-based object tracking," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, pp. 564-567, May, 2003. https://doi.org/10.1109/TPAMI.2003.1195991
  39. T. Xiong and C. Debrunner, "Stochastic car tracking with line and color based features," IEEE Transactions on Intelligent Transportation Systems, vol. 5, no. 4, pp.324-328, January, 2005.
  40. I. Szottka and M. Butenuth, "Advanced Particle Filtering for Airborne Vehicle Tracking in Urban Areas, " IEEE Geoscience and Remote Sensing Letters, vol. 11, no. 3, pp. 686-690, March, 2014. https://doi.org/10.1109/LGRS.2013.2274939
  41. R. Hostettler and P. M. Djuric, "Vehicle Tracking Based on Fusion of Magnetometer and Accelerometer Sensor Measurements With Particle Filtering," IEEE Transactions on Vehicular Technology, vol. 64, no. 11, pp. 4917-4928, November, 2015. https://doi.org/10.1109/TVT.2014.2382644
  42. P. Liu, W. Li, Y. Wang and H. Ni, "On-road multi-vehicle tracking algorithm based on an improved particle filter, " IET Intelligent Transport Systems, vol. 9, no. 4, pp. 429-441, May, 2015. https://doi.org/10.1049/iet-its.2014.0088
  43. J. Q. Li, R. H. Zhao, J. L. Chen, C. Y. Zhao, Y. P. Zhu, "Target tracking algorithm based on adaptive strong tracking particle filter, " IET Science, Measurement & Technology, vol. 10, no. 7, pp. 704-710, October, 2016. https://doi.org/10.1049/iet-smt.2016.0044
  44. L. Ubeda-Medina, A. Garcia-Fernandez, J. Grajal, "Adaptive auxiliary particle filter for track-before-detect with multiple targets," IEEE Transactions on Aerospace and Electronic Systems, vol.PP, no.99, pp.1-1, April, 2017. https://doi.org/10.1109/TAES.2017.2691958
  45. R. Tavoli, E. Kozegar, M. Shojafar, H. Soleimani and Z. Pooranian, "Weighted PCA for improving Document Image Retrieval System based on keyword spotting accuracy, " in Proc. of 36th Int. Conf. on Telecommunications and Signal Processing, pp. 773-777, July 2-4, 2013.
  46. M. E. Yildirim, F. I. Ince, Y. B. Salman, J. K. Song, J. S. Park, B. W. Yoon, "Direction based modified particle filter for vehicle tracking," ETRI Journal, vol. 38, no. 2, pp. 356 - 364, April, 2016. https://doi.org/10.4218/etrij.16.0115.0181
  47. S.K. Zhou, R. Chellappa, and B. Moghaddam, "Visual Tracking and Recognition Using Appearance-Adaptive Models in Particle Filters," IEEE Transactions on Image Processing, vol. 13, no. 11, pp. 1491-1506, November, 2004. https://doi.org/10.1109/TIP.2004.836152
  48. M. Bolic, S.J. Hong and P. M. Djuric, "Performance and complexity analysis of adaptive particle filtering for tracking applications, " in Proc. of the 36th Asilomar Conf. on Signals, Systems and Computers, pp.853-857, November 3-6, 2002.
  49. N. Saunier, H. Ardo, J.-P. Jodoin, A. Laureshyn, M. Nilsson, A. Svensson, L. Fernando Miranda-Moreno, G.-A. Bilodeau, and K. Astro, "Public video data set for road transportation applications," in Transportation Research Board Annual Meeting Compendium of Papers, pp. 14-2379, November 15, 2014.
  50. B.Babenko, M.H. Yang, and S. Belongie, "Robust Object Tracking with Online Multiple Instance Learning," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.33, no. 7, pp. 1619-1632, December, 2011.
  51. Y. Wu, J. Lim and M. H. Yang, "Online Object Tracking: A Benchmark," in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp. 2411-2418, June 23-28, 2013.