Day/Night Detector for Vehicle Tracking in Traffic Monitoring Systems
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.
[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).
[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).
@article{"International Journal of Information, Control and Computer Sciences:71876", author = "M. Taha and Hala H. Zayed and T. Nazmy and M. Khalifa", title = "Day/Night Detector for Vehicle Tracking in Traffic Monitoring Systems", 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.", volume = "10", number = "1", pages = "98-7", }
