Moving vehicle detection from video sequences for Traffic Surveillance System
Abstract
In the current scenario, Intelligent Transportation Systems play a significant role in smart city platform. Automatic moving vehicle detection from video sequences is the core component of the automated traffic management system. Humans can easily detect and recognize objects from complex scenes in a flash. Translating that thought process to a machine, however, requires us to learn the art of object detection using computer vision algorithms. This paper solves the traffic issues of the urban areas with an intelligent automatic transportation system. This paper includes automatic vehicle counting with the help of blob analysis, background subtraction with the use of a dynamic autoregressive moving average model, identify the moving objects with the help of a Boundary block detection algorithm, and tracking the vehicle. This paper analyses the procedure of a video-based traffic congestion system and divides it into greying, binarisation, de-nosing, and moving target detection. The investigational results show that the planned system can provide useful information for traffic surveillance.
Downloads
References
R. Babitha Lincy, Gayathri, R. Optimally configured convolutional neural network for Tamil Handwritten Character Recognition by improved lion optimization model. Multimedia Tools and Application (2020). https://doi.org/10.1007/s11042-020-09771-z
S. Cheung and C. Kamath, Robust Techniques for Background Subtraction in Urban Traffic Video, Video Communications and Image Processing, SPIE Electronic Imaging, San Jose, January 2004.
B. Qi, M. Ghazal, and A. Amer, “Robust global motion estimation oriented to video object segmentation,” IEEE Transactions on Image Processing, vol. 17, no. 6, pp. 958–967, 2008.
H. Eng, J. Wang, A. Kam, and W. Yau, “Novel Regionbased Modeling for Human Detection within High Dynamic Aquatic Environment,” Proceedings on CVPR, 2004.
Thou-Ho Chen, Jun-Liang Chen, Chin-Hsing Chen and Chao-Ming Chang, "Vehicle Detection and Counting by Using Headlight Information in the Dark Environment" in IEEE 2007 International Conference on Intelligent Information Hiding and Multimedia Signal Processing IIHMSP07, Kaohsiung, Taiwan, Nov. 26-28, 2007. pp. 519-522.
Surendra Gupte, Osama Masoud, Robert F. K. Martin, and Nikolaos P. Papanikolopoulos, Detection and Classification of Vehicles, in Proc. IEEE Transactions On Intelligent Transportation Systems, Vol. 3, NO. 1, March 2002.
M. Cristani, M. Bicegi, and V. Murino, “Integrated Region- and Pixel based Approach to Background Modeling”, Proceedings of the MOTION, 2002.
K. P. Karmann and A. von Brandt, “Moving object recognition using an adaptive background memory,” in Proc. Time-Varying Image Processing and Moving Object Recognition,vol. 2, V. Capellini, Ed., 1990.
O. Brouard, F. Delannay, V. Ricordel, and D. Barba, “Spatio-temporal segmentation and regions tracking of high definition video sequences based on a markov random field model,” in Proceedings of the IEEE International Conference on Image Processing (ICIP '08), pp. 1552–1555, October 2008.
N. Kanhere, S. Pundlik, S. Birchfield, Vehicle Segmentation and Tracking from a Low-Angle Off-Axis Camera, IEEE Conference on Computer Vision and Pattern Recognition, San Diego, June 2005.
H. S. Mohana, Aswatha Kumar and Shiva kumar Malnad College of Engineering, Hassan, Karnataka,” Vehicle Counting And Classification Using Kalman Filter And Pixel Scanner Technique And Its Verification With Optical Flow Estimation”, Global Journal of Computer Science and Technology 46 Vol. 10 Issue 8 Ver. 1.0 September 2010.
Vibha L, Chetana Hegde, P Deepa Shenoy, Venugopal K R, L M Patnaik,” Dynamic Object Detection, Tracking and Counting in Video Streams for Multimedia Mining”, IAENG International Journal of Computer Science35:3 IJCS_35_3_16, august 2008.
Neeraj K. Kanhere, Stanley T. Birchfield, Wayne A. Sarasua, Tom C. Whitney, Real-time detection and tracking of vehicle base fronts for measuring traffic counts and speeds on highways", Transportation Research Record, No. 1993. (2007).
A. J. Kun and Z. Vamossy, "Traffic monitoring with computer vision," Proc. 7th Int. Symp. Applied Machine Intelligence and Informatics (SAMI 2009).
P. Wang and H. Qiao, “Online appearance model learning and generation for adaptive visual tracking,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 21, no. 2, pp. 156–169, 2011.
D. Jansari and S. Parmar, “Novel object detection method based on optical flow,” in Proceedings of the 3rd International Conference on Emerging Trends in Computer and Image Processing (ICETCIP '13), Kuala Lumpur, Malaysia, 2013.
M. Vargas, Member, IEEE, S. L. Toral, Senior Member, IEEE ; F. Barrero, Senior Member, IEEE;J.M. Milla, “An Enhanced Background Estimation Algorithmfor Vehicle Detection in Urban Traffic Video”, Proceedings of the 11th International IEEE Conference on Intelligent Transportation Systems Beijing, China, October 12-15, 2008.
D. Beymer, P. McLauchlan, B. Coifman, and J. Malik, “A real-time computer vision system for measuring traffic parameters,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, Puerto Rico, June 1997, pp. 496–501.
E. Sankaralingam, V. Thangaraj, S. Vijayamani, and N. Palaniswamy, “Video compression using multiwavelet and multistage vector quantization,” International Arab Journal of Information Technology, vol. 6, no. 4, pp. 385–393, 2009.
Jency Rubia, J., Sathish Kumar, G.A.: A high speed fixed-width floating point multiplier using RLNS algorithm. Int. J. Electr. Eng. Educ. 1–9 (2018).
T. Senst, V. Eiselein, and T. Sikora, “Robust local optical flow for feature tracking,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 22, no. 9, pp. 1377–1387, 2012.
Y. Sheikh, O. Javed, and T. Kanade, “Background subtraction for freely moving cameras,” in Proceedings of the IEEE 12th International Conference on Computer Vision, pp. 1219–1225, Kyoto, Japan, 2009.
Kanhere, Birchfield, Sarasua, Whitney, "Real-Time Detection and Tracking of Vehicle Base Fronts for Measuring Traffic Counts and Speeds on Highways" in Transportation Research Board Annual Meeting, Washington, D.C., January 2007. TRB Paper Number: 07-3308.
A. Ghosh, B. N. Subudhi, and S. Ghosh, “Object detection from videos captured by moving camera by fuzzy edge incorporated markov random field and local histogram matching,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 22, no. 8, pp. 1127–1135, 2012.
K. Kalirajan, M. Sudha, V. Rajeshkumar, and S. S. Jamaesha, “Adaptive visual tracking system using artificial intelligence,” in Proceedings of the IEEE/OSA/IAPR International Conference on Informatics, Electronics and Vision (ICIEV '12), pp. 954–957, IEEE, May 2012.
S. Johnson and A. Tews, “Real-time object tracking and classification using a static camera,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA '09), Kobe, Japan, May 2009.
Jency Rubia J., Babitha Lincy R. (2021) Digital Image Restoration Using Modified Richardson-Lucy Deconvolution Algorithm. In: Chen JZ., Tavares J., Shakya S., Iliyasu A. (eds) Image Processing and Capsule Networks. ICIPCN 2020. Advances in Intelligent Systems and Computing, vol 1200. Springer, Cham. https://doi.org/10.1007/978-3-030-51859-2_10.
M.Sankari and C. Meena ,“Adaptive Background Estimation and object detection applying in Automated visual surveillance” (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 4, July 2010.
This work is licensed under a Creative Commons Attribution 4.0 International License.
