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3D Research

, 7:10 | Cite as

Pixel Intensity Based Cumulative Features for Moving Object Tracking (MOT) in Darkness

  • Tanzila Saba
3DR Express

Abstract

Moving Object Tracking (MOT) is one of the frequent used tasks in computer vision systems and on the same time a challenging issue particularly in darkness. Vehicle tracking involves finding new position of vehicles in consecutive frames. This paper presents MOT algorithm that is developed for advanced driver safety applications like automatic high beam control, forward collision warning. Accordingly, the proposed approach targets vehicle tracking in the dark environment. Hence, a camera is mounted on the host vehicle to capture video frames of the traffic ahead. The scope involves tracking of both oncoming and preceding vehicles. The vehicles are tracked in consecutive frames using grayscale information and robust structure features. The features representation of the vehicle region is based on cumulative pixel intensity information. The implementation for feature extraction is optimized by using a dynamic programming approach to meet the constraints of a real time application. Simulation results thus obtained are promising in state of the art.

Keywords

Advanced driver assistance systems Moving object tracking Dynamic programming Feature mining Classification 

References

  1. 1.
    Saba, T., & Rehman, A. (2012). Effects of artificially intelligent tools on pattern recognition. International Journal of Machine Learning and Cybernetics, 4(2), 155–162. doi: 10.1007/s13042-012-0082-z.CrossRefGoogle Scholar
  2. 2.
    Rehman, A., & Saba, T. (2014). Features extraction for soccer video semantic analysis: Current achievements and remaining issues. Artificial Intelligence Review, 41(3), 451–461. doi: 10.1007/s10462-012-9319-1.CrossRefGoogle Scholar
  3. 3.
    Saba, T., & Rehman, A. (2012). Machine learning and script recognition (pp. 51–56). Saarbrücken: Lambert Academic Publisher.Google Scholar
  4. 4.
    Alcantarilla, P. F., Bergasa, L. M., Jiménez, P., Parra, I., Llorca, D. F., Sotelo, M., & Mayoral, S. (2011). Automatic lightbeam controller for driver assistance. Machine Vision and Applications, 22(5), 819–835.CrossRefGoogle Scholar
  5. 5.
    Saba, T., Rehman, A., Altameem, A., & Uddin, M. (2014). Annotated comparisons of proposed preprocessing techniques for script recognition. Neural Computing and Applications, 25(6), 1337–1347. doi: 10.1007/s00521-014-1618-9.CrossRefGoogle Scholar
  6. 6.
    Goormer, S., Muller, D., Hold, S., Meuter, M., & Kummert, A. (2009). Vehicle recognition and TTC estimation at night based on spotlight pairing. In ITSC’09: Proceeding of IEEE International Conference on Intelligent Transportation Systems (pp. 1–6).Google Scholar
  7. 7.
    Schadel, C. & Falb, D. (2007). Smartbeam: A high-beam assist. In Proceedings of International Symposium on Automotive Lighting. DarmstadtGoogle Scholar
  8. 8.
    Fang, C. Y., Chen, S. W., & Fuh, C. S. (2003). Road sign detection and tracking. IEEE Transactions on Vehicular Technology, 52(5), 1329–1341.CrossRefGoogle Scholar
  9. 9.
    Bellotti, C., Bellotti, F., De Gloria, A., Andreone, L., & Mariani, M. (2004). Developing a near infrared based night vision system. In Proceedings of IEEE Intelligent Vehicles Symposium. Google Scholar
  10. 10.
    Ge, J., Luo, Y., & Tei, G. (2009). Real-time pedestrian detection and tracking at nighttime for driver-assistance systems. IEEE Transactions on Intelligent Transportation Systems, 10(2), 283–298.CrossRefGoogle Scholar
  11. 11.
    Geiger, D., Gupta, A., Costa, L. A., & Vlontzos, J. (1995). Dynamic programming for detecting, tracking, and matching deformable contours. IEEE Transactions on PAMI, PAMI-17(3), 294–302.CrossRefGoogle Scholar
  12. 12.
    O’Malley, R., Jones, E., & Glavin, M. (2010). Rear-lamp vehicle detection and tracking in low exposure color video for night conditions. IEEE Transactions on Intelligent Transportation Systems, 11(2), 453–462.CrossRefGoogle Scholar
  13. 13.
    López, A., Hilgenstock, J., Busse, A., Baldrich, R., Lumbreras, F., & Serrat, J. (2008). Nighttime vehicle detection for intelligent headlight control. In Advanced Concepts for Intelligent Vision Systems (pp. 113–124).Google Scholar
  14. 14.
    Chen, Y.-L., Chiang, H.-H., Chiang, C.-Y., Liu, C.-M., Yuan, S.-M., & Wang, J.-H. (2012). A vision-based driver nighttime assistance and surveillance system based on intelligent image sensing techniques and a heterogamous dual-core embedded system architecture. Sensors, 12(3), 2373–2399. doi: 10.3390/s120302373.CrossRefGoogle Scholar
  15. 15.
    Ogura, R. & Ohashi, G. (2012). Vehicles detection based on extremas in nighttime driving scene. In Consumer Electronics (I), 2012 IEEE (pp. 679–682)Google Scholar
  16. 16.
    Wang, J., Sun, X., & Guo, J. (2013). A region tracking-based vehicle detection algorithm in nighttime traffic scenes. Sensors, 13(12), 16474–16493.CrossRefGoogle Scholar
  17. 17.
    Soleimanizadeh, S., Mohamad, D., Saba, T., & Rehman, A. (2015). Recognition of partially occluded objects based on the three different color spaces (RGB, YCbCr, HSV). 3D Research, 6(3), 1–10. doi: 10.1007/s13319-015-0052-9.CrossRefGoogle Scholar
  18. 18.
    Muhsin, Z. F., Rehman, A., Altameem, A., Saba, T., & Uddin, M. (2014). Improved quadtree image segmentation approach to region information. The Imaging Science Journal, 62(1), 56–62. doi: 10.1179/1743131X13Y.0000000063.CrossRefGoogle Scholar
  19. 19.
    Chen, Y., Chen, Y., Chen, C., & Wu, B. (2006). Nighttime vehicle detection for driver assistance and autonomous vehicles. In Proceedings of International Conference on Pattern Recognition (Vol. 1, pp. 687–690)Google Scholar
  20. 20.
    Chen, Y. L., Wu, B. F., Fan, C. J. (2009). Real-time vision based multiple vehicle detection and tracking for nighttime traffic surveillance. In IEEE International Conference on Systems, Man and Cybernetics (SMC 2009) (pp. 3352–3358). doi: 10.1109/ICSMC.2009.5346191.
  21. 21.
    Viola, P. & Jones, M. J. (2001). Rapid object detection using a boosted cascade of simple features. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Vol. 1, pp. 511–518)Google Scholar
  22. 22.
    Li, Y., Haas, N., & Pankanti, S. (2011). Intelligent headlight control using learning-based approaches. In Intelligent Vehicles Symposium, IEEE (pp. 722–727). doi: 10.1109/IVS.2011.5940541.
  23. 23.
    Connell, J. H., Herta, B. W., Pankanti, S., Hess, H., Pliefke, S. (2011). A fast and robust intelligent headlight controller for vehicles. In Intelligent Vehicles Symposium (IV), IEEE (pp. 703–708). doi: 10.1109/IVS.2011.5940492
  24. 24.
    Elarbi-Boudihir, M., Rehman, A., & Saba, T. (2011). Video motion perception using optimized Gabor filter. International Journal of Physical Sciences, 6(12), 2799–2806.Google Scholar

Copyright information

© 3D Research Center, Kwangwoon University and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.College of Computer and Information SciencesPrince Sultan UniversityRiyadhKingdom of Saudi Arabia

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