Pixel Intensity Based Cumulative Features for Moving Object Tracking (MOT) in Darkness
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.
KeywordsAdvanced driver assistance systems Moving object tracking Dynamic programming Feature mining Classification
- 3.Saba, T., & Rehman, A. (2012). Machine learning and script recognition (pp. 51–56). Saarbrücken: Lambert Academic Publisher.Google Scholar
- 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.Schadel, C. & Falb, D. (2007). Smartbeam: A high-beam assist. In Proceedings of International Symposium on Automotive Lighting. DarmstadtGoogle Scholar
- 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
- 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.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.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
- 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.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.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.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.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.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