Day/Night Detector for Vehicle Tracking in Traffic Monitoring Systems
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Day/Night Detector for Vehicle Tracking in Traffic Monitoring Systems

Authors: M. Taha, Hala H. Zayed, T. Nazmy, M. Khalifa

Abstract:

Recently, traffic monitoring has attracted the attention of computer vision researchers. Many algorithms have been developed to detect and track moving vehicles. In fact, vehicle tracking in daytime and in nighttime cannot be approached with the same techniques, due to the extreme different illumination conditions. Consequently, traffic-monitoring systems are in need of having a component to differentiate between daytime and nighttime scenes. In this paper, a HSV-based day/night detector is proposed for traffic monitoring scenes. The detector employs the hue-histogram and the value-histogram on the top half of the image frame. Experimental results show that the extraction of the brightness features along with the color features within the top region of the image is effective for classifying traffic scenes. In addition, the detector achieves high precision and recall rates along with it is feasible for real time applications.

Keywords: Day/night detector, daytime/nighttime classification, image classification, vehicle tracking, traffic monitoring.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1338640

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[1] Wang, G., Xiao, D., Gu, J.: Review on Vehicle Detection based on Video for Traffic Surveillance. In: IEEE International Conference on Automation and Logistics (ICAL 2008), pp. 2961-2966. IEEE, (2008).
[2] Chiu, C.-C., Ku, M.-Y., Wang, C.-Y.: Automatic Traffic Surveillance System for Vision-Based Vehicle Recognition and Tracking. Journal of Information Science and Engineering 26,611-629 (2010).
[3] Neelima, D., Mamidisetti, G.: A Computer Vision Model for Vehicle Detection in Traffic Surveillance. International Journal of Engineering Science & Advanced Technology (IJESAT) 2,1203-1209 (2012).
[4] Foresti, G.L., Snidaro, L.: Vehicle Detection and Tracking for Traffic Monitoring. Image Analysis and Processing (ICIAP 2005), pp. 1198- 1205. Springer (2005).
[5] Rodríguez, T., García, N.: An Adaptive, Real-time, Traffic Monitoring System. Machine Vision and Applications 21,555-576 (2010).
[6] Hadi, R.A., Sulong, G., George, L.E.: Vehicle Detection and Tracking Techniques: A Concise Review. arXiv preprint arXiv:1410.5894 (2014).
[7] Sivaraman, S., Trivedi, M.M.: A Review of Recent Developments in Vision-based Vehicle Detection. In: Intelligent Vehicles Symposium, pp. 310-315. (2013.)
[8] Kovacic, K., Ivanjko, E., Gold, H.: Computer Vision Systems in Road Vehicles: a Review. In: the Proceedings of the Croatian Computer Vision Workshop. (2013).
[9] Cheng, H.-Y., Liu, P.-Y., Lai, Y.-J.: Vehicle Tracking in Daytime and Nighttime Traffic Surveillance Videos. In: 2nd International Conference on Education Technology and Computer (ICETC), pp. V5-122-V125- 125. IEEE, (2010).
[10] Robert, K.: Video-based Traffic Monitoring at Day and Night Vehicle Features Detection Tracking. In: 12th International IEEE Conference on Intelligent Transportation Systems (ITSC'09), pp. 1-6. IEEE, (2009).
[11] Salvi, G.: An Automated Nighttime Vehicle Counting and Detection System for Traffic Surveillance. In: International Conference on Computational Science and Computational Intelligence (CSCI), pp. 131- 136. IEEE, (2014).
[12] Wang, J., Sun, X., Guo, J.: A Region Tracking-based Vehicle Detection Algorithm in Nighttime Traffic Scenes. Sensors 13,16474-16493 (2013).
[13] Wang, Y., Hu, B.-G.: Hierarchical Image Classification using Support Vector Machines. In: The 5th Asian Conference on Computer Vision (ACCV 2002), pp. 23-25. (2002).
[14] Maddern, W., Stewart, A.D., McManus, C., Upcroft, B., Churchill, W., Newman, P.: Illumination Invariant Imaging: Applications in Robust Vision-based Localisation, Mapping and Classification for Autonomous Vehicles. In: Proc. of Workshop on Visual Place Recognition in Changing Environments, IEEE International Conference on Robotics and Automation (ICRA). (2014).
[15] Chen, C.-H., Chen, L.-H., Takama, Y.: Proposal of Situation-based Clustering of Sightseeing Spot Images based on ROI-based Color Feature Extraction. In: Conference of Japanese Society for Artificial Intelligence. (2012).
[16] Saha, B., Davies, D., Raghavan, A.: Day Night Classification of Images using Thresholding on HSV Histogram. Google Patents (2012).
[17] Raghavan, A., Liu, J., Saha, B., Price, R.: Reference Image-Independent Fault Detection in Transportation Camera Systems for Nighttime Scenes. In: 15th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 963-968. IEEE, (2012).
[18] Zhou, N., Dong, W., Wang, J., Jean-Claude, P.: Simulating Human Visual Perception in Nighttime Illumination. Tsinghua Science & Technology 14,133-138 (2009).
[19] Kuthan, S.: Extraction of Aattributes, Nature and Context of Images. Pattern Recognition and Image Processing Group, Institute of Computer Aided Automation, Vienna University of Technology (2005).
[20] Taha, M., H Zayed, H., E Khalifa, M., Nazmy, T.: Moving Shadow Removal for Multi-Objects Tracking in Outdoor Environments. International Journal of Computer Applications (IJCA) 97,43-51 (2014).
[21] Taha, M., Zayed, H.H., Nazmy, T., Khalifa, M.: Multi-Vehicle Tracking Under Day and Night Illumination. The International Journal of Scientific & Engineering Research (IJSER) 5,837-848 (2014).
[22] Taha, M., Zayed, H.H., Nazmy, T., Khalifa, M.: An Efficient Method for Multi Moving Objects Tracking at Nighttime. The International Journal of Computer Science Issues (IJCSI) 11,17-27 (2014).
[23] Mouats, T., Aouf, N.: Fusion of Thermal and Visible Images for Day/Night Moving Objects Detection. In: Sensor Signal Processing for Defence (SSPD), 2014, pp. 1-5. IEEE, (2014).