@article{(Open Science Index):https://publications.waset.org/pdf/10003389,
	  title     = {Day/Night Detector for Vehicle Tracking in Traffic Monitoring Systems},
	  author    = {M. Taha and  Hala H. Zayed and  T. Nazmy and  M. Khalifa},
	  country	= {},
	  institution	= {},
	  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.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {10},
	  number    = {1},
	  year      = {2016},
	  pages     = {98 - 104},
	  ee        = {https://publications.waset.org/pdf/10003389},
	  url   	= {https://publications.waset.org/vol/109},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 109, 2016},
	}