An Efficient Approach for Detecting and Classifying Moving Vehicles in a Video Based Monitoring System | ||
| Engineering and Technology Journal | ||
| Article 4, Volume 38, 6A, June 2020, Pages 832-845 PDF (1.47 M) | ||
| Document Type: Research Paper | ||
| DOI: 10.30684/etj.v38i6A.438 | ||
| Authors | ||
| Sajidah S. Mahmood* 1; Laith J. Saud2 | ||
| 1Computer Engineering Branch, Department of Control and Systems Engineering, University of Technology-Iraq. | ||
| 2Computer Engineering Branch, Department of Control and Systems Engineering, University of Technology-Iraq | ||
| Abstract | ||
| Moving objects detection, type recognition, and traffic analysis in video-based surveillance systems is an active area of research which has many applications in road traffic monitoring. This paper is on using classical approaches of image processing to develop an efficient algorithm for computer vision based on traffic surveillance system that can detect and classify moving vehicles, besides serving some other traffic analysis issues like finding vehicles speed and heading, tracking specified vehicles, and finding traffic load. The algorithm is designed to be flexible for modification to fulfill the changes in design objectives, having limited computation time, giving good accuracy, and serves inexpensive implementation. A 92% of success is achieved for the considered test, with the missed cases being abnormal that are not defined to the algorithm. The computation time, with a platform (hardware and software) dependent, the algorithm took to produce results was parts of milliseconds. A CNN based deep learning classifier was built and evaluated to judge the feasibility of involving a modern approach in the design for the targeted aims in this work. The modern NN based deep learning approach is very powerful and represents the choice for many very sophisticated applications, but when the purpose is restricted to limited requirements, as it is believed the case is here, the reason will be to use the classical image processing procedures. In making choice, it is important to consider, among many things, accuracy, computation time, and simplicity of design, development, and implementation. | ||
| Keywords | ||
| object detection; object recognition; Background Subtraction; Image Processing; traffic analysis | ||
| References | ||
|
[1] A.l. Adrian, P. Ismet and P. Petru, “An overview of intelligent surveillance systems development,” 13th International Symposium on Electronics and Telecommunications ISETC, Timisoara, Romania, pp. 1–6, 2018. [2] S. Memon, S. Bhatti, L.A. Thebo, M. M. B. Talpur, and M. A. Memon, “A video based vehicle detection, counting and classification system,” International Journal of Image, Graphics & Signal Processing, Vol. 10, No. 9, pp. 34-41, 2018. [3] Y. Li, G. Liu, and S. Chen, “Detection of moving object in dynamic background using gaussian max-pooling and segmentation constrained RPCA,” Computer Science arXiv Articles [Online], arXiv: 1709.00657, Sep., 2017. [4] Y. Wang, X. Ban, H. Wang, D. Wu, H. Wang, S. Yang, S. Liu, and J. Lai, “detection and classification of moving vehicle from video using multiple spatio-temporal features,” IEEE Access, Vol. 7, pp. 80287–80299, June, 2019. [5] S. Gupte, O. Masoud, R.F.K. Martin, and N.P. Papanikolopoulos, “Detection and classification of vehicles,” IEEE Trans. On Intell. Transp. Syst., Vol. 3, No. 1, pp. 37–47, March, 2002. [6] W. Xiao, B. Vallet, K. Schindler, and N. Paparoditis, “Street-side vehicle detection, classification and change detection using mobile laser scanning data,” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 114, pp. 166-178, April, 2016. [7] A. Raychaudhuri S. Maity, A. Chakrabarti, and D. Bhattacharjee, “Detection of moving objects in video using block-based approach,” Springer, 1st Ed, Singapore, Ch. 8, pp. 151-167, 2019. [8] H.S. Parekh, D.G. Thakore, and U.K. Jaliya, “A Survey on object detection and tracking methods,” International Journal of Innovative Research in Computer and Communication Engineering, Vol. 2, No.2, pp. 2970-2979, Feb., 2014. [9] X. Wang, “Deep learning in object recognition, detection, and segmentation,” Now Publishers, 1st ed, Hanover, 2016. [10] S. Johan, and S. Sakhare, “Image processing techniques for object tracking in video surveillance- a survey,” Int. Conf. on Pervasive Computing ICPC, Pune, pp. 1-6, 2015. [11] G. Sindhuja, and R. Devi, “A survey on detection and tracking of objects in video sequence,” Int. Journal of Eng. Research and General Science, Vol. 3, No. 2, pp. 418–426, 2015. [12] S. Shah, V. Adhikan, and V. Pokhriyal, “Motion detection algorithm based on background subtraction,” International Journal of Scientific & Engineering Research, Vol. 4, No. 8, pp. 1945-1948, Aug., 2013. [13] M. A. Jayaram, and H. Fleyeh, “Convex hulls in image processing: a scoping review,” American Journal of Intelligent Systems, Vol. 6, No. 2, pp. 48–58, May, 2016. [14] X. Poda, and O. Qirici, “Shape detection and classification using open CV and Arduino uno,” 3rd Inter. Conference on Recent Trends and Applications in Computer Science and Information Technology, Tirana, pp. 128–136, 2018. [15] S.P. Patil, “Techniques and methods for detection and tracking of moving object in a vide,” Inter. Journal of Innovative Research in Computer and Communication Engineering, Vol. 4, No. 5, May, 2016. [16] T. Chilimbi, Y. Suzue, J. Apacible, and K. Kalyanaraman, “Building an efficient and scalable deep learning training system,” 11th USENIX Symposium on Operating Systems Design and Implementation, Broomfield, pp. 571–582, 2014. [17] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Communications of the ACM, Vol. 60, No. 6, pp. 84-90, June, 2017 | ||
|
Statistics Article View: 360 PDF Download: 171 |
||