Detecting, identifying, and counting vehicles based on deep learning algorithms in video surveillance systems

Akoushideh, Alireza; Sadat, Seyyed Shafiullah; Shahbahrami, Asadollah

doi:10.48301/jear.2024.459050.1027

Detecting, identifying, and counting vehicles based on deep learning algorithms in video surveillance systems

Document Type : Original Article

Authors

Alireza Akoushideh ¹

Seyyed Shafiullah Sadat ²

Asadollah Shahbahrami ³

¹ Assistant Professor, Electronics Engineering, Technical and Vocational University, Tehran, Iran.

² Faculty Member, Computer Engineering, Kabul Polytechnic University, Kabul, Afghanistan.

³ Professor, Computer Engineering, University of Guilan, Rasht, Iran.

10.48301/jear.2024.459050.1027

Abstract

Detection, identification, and automatic counting of vehicles using video surveillance cameras plays an important role in the field of intelligent transportation management. Despite the progress that researchers have made in these cases, its operational implementation still faces challenges such as "various environmental conditions", "unbalanced data sets", "accuracy" and "speed". Therefore, research can be useful in solving these issues. The proposed algorithm for detection, classification, and counting will be based on deep learning. In this research, after applying the proposed initial preprocessing algorithm, we use the YOLO algorithm to detect and classify vehicles. The DeepSORT algorithm is also used to track several vehicles at the same time. For the accurate counting of vehicles, a developed method is also proposed to increase the processing accuracy. By applying the proposed pre-processing and counting techniques, the practical results show that the "call" criterion in the video with detection at night challenge has been increased to 99.18%.

Keywords

Machine Vision

Deep Learning

Vehicle Detection

Car Classification

Car Counting

Subjects

Computer Engineering

[1] Kaffash, S., Nguyen, A. T., & Zhu, J. (2021). Big data algorithms and applications in intelligent transportation system: A review and bibliometric analysis. International Journal of Production Economics, 231, 107868. https://doi.org/10.1016/j.ijpe.2 020.107868

[2] Pourjavan, K. (2019). Explanation of Smart City and Urban Smart Transportation Solutions. Quarterly Scientific Journal of National University of skills, 16(1), 15-34. https:// karafan.tvu.ac.ir/article_100529.html?lang=en

[3] Akoushideh, A., Tourani, A., Shahbahrami, A., & Masoumnezhad, M. (2021). Design and Implementation of Automatic License Plate Recognition System for Security Gates. Quarterly Scientific Journal of National University of skills, 18(3), 237-252. https: //doi.org/10.48301/kssa.2021.130288

[4] Moradi, A., Shahbahrami, A., & Akoushideh, A. (2021). An unsupervised approach for traffic motion patterns extraction. Institution of Engineering and Technology Image Processing, 15(2), 428-442. https://doi.org/10.1049/ipr2.12033

[5] Galea, S., Seychell, D., & Bugeja, M. (2020, December 3-5). A Survey of Intelligent transportation systems based Modern Object Detectors Under Night-time Conditions [Conference session]. 3rd International Conference on Intelligent Sustainable Systems ,Thoothukudi, India. https://doi.org/10.1109/ICISS49785.2020.9316076

[6] Tourani, A., Shahbahrami, A., & Akoushideh, A. (2017, November 22-23). Challenges of video-based vehicle detection and tracking in intelligent transportation systems [Conference session]. 2nd International Conference on Soft Computing, Rudsar, Guilan, Iran. https://civilica.com/doc/696790/

[7] Shetty, A. K., Saha, I., Sanghvi, R. M., Save, S. A., & Patel, Y. J. (2021, April 2-4). A Review: Object Detection Models [Conference session]. 6th International Conference for Convergence in Technology, Maharashtra, India. https://doi.org/10.1109/I2C T51068.2021.9417895

[8] Manikandan, R., & Ramakrishnan, R. (2013). Video object extraction by using background subtraction techniques for sports applications. Digital Image Processing, 5(9), 435-440. https://www.researchgate.net/publication/260288356_Video_Obje ct_Extraction_by_Using_Background_Subtraction_Techniques_for_Sports_Applications

[9] Li, Q-L., & He, J-F. (2011). Vehicles detection based on three-frame-difference method and cross-entropy threshold method. Jisuanji Gongcheng/ Computer Engineering, 37(4), 172-174. https://doi.org/10.3969/j.issn.1000-3428.2011.04.062

[10] Liu, Y., Lu, Y., Shi, Q., & Ding, J. (2013, December 14-15). Optical Flow Based Urban Road Vehicle Tracking [Conference session]. 2013 Ninth International Conference on Computational Intelligence and Security, Emeishan, China. https://doi.org/ 10.1109/CIS.2013.89

[11] Xiao, Y., Tian, Z., Yu, J., Zhang, Y., Liu, S., Du, S., & Lan, X. (2020). A review of object detection based on deep learning. Multimedia Tools and Applications, 79(33), 23729-23791. https://doi.org/10.1007/s11042-020-08976-6

[12] Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014, June 23-28). Rich feature hierarchies for accurate object detection and semantic segmentation [Conference session]. Proceedings of the Institute of Electrical and Electronics Engineers conference on computer vision and pattern recognition, Ohio, USA. https://doi.org/10.110 9/CVPR.2014.81

[13] Ren, S., He, K., Girshick, R. B., & Sun, J. (2015, December 7-12). Faster r-cnn: Towards real-time object detection with region proposal networks [Conference session]. Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems, Montreal, Quebec, Canada. http://papers.nip s.cc/paper/5638-faster-r-cnn-towards-real-time-object-detection-with-regio n-proposal-networks

[14] Lin, T. Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2017, October 22-29). Focal Loss for Dense Object Detection [Conference session]. 2017 Institute of Electrical and Electronics Engineers International Conference on Computer Vision Venice, Italy. https://doi.org/10.1109/ICCV.2017.324

[15] Redmon, J., & Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv, 1-6. https://doi.org/10.48550/arXiv.1804.02767

[16] Bochkovskiy, A., Wang, C-Y., & Liao, H-Y. M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv, 1-17. https://doi.org/10.48550/arXiv.2004.10934

[17] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C-Y., & Berg, A. C. (2016). SSD: Single Shot MultiBox Detector. In B. Leibe, J. Matas, N. Sebe, & M. Welling (Eds.), Computer Vision – ECCV 2016 (pp. 21-37). Springer International Publishing. https://doi.org/10.1007/978-3-319-46448-0_2

[18] Song, H., Liang, H., Li, H., Dai, Z., & Yun, X. (2019). Vision-based vehicle detection and counting system using deep learning in highway scenes. European Transport Research Review, 11(1), 51. https://doi.org/10.1186/s12544-019-0390-4

[19] Yadav, G., Maheshwari, S., & Agarwal, A. (2014, September 24-27). Contrast limited adaptive histogram equalization based enhancement for real time video system [Conference session]. 2014 International Conference on Advances in Computing, Communications and Informatics, Delhi, India. https://doi.org/10.1109/ICACC I.2014.6968381

[20] Gedraite, E. S., & Hadad, M. (2011, September 14-16). Investigation on the effect of a Gaussian Blur in image filtering and segmentation [Conference session]. International Symposium on Electronics in Marine 2011, Zadar, Croatia. https://ieeexplore.i eee.org/abstract/document/6044249

[21] Hasanpour, H., & Asadi Amiri, S. (2015). Comprehensive concepts of digital image processing along with implementation of algorithms with MATLAB (S. Gholamian, Ed.). Shahrood University of Technology. https://www.gisoom.com/book/11377594

[22] Sejr, J. H., Schneider-Kamp, P., & Ayoub, N. (2021). Surrogate Object Detection Explainer (SODEx) with YOLOv4 and LIME. Machine Learning and Knowledge Extraction, 3(3), 662-671. https://doi.org/10.3390/make3030033

[23] Wojke, N., Bewley, A., & Paulus, D. (2017, September 17-20). Simple online and realtime tracking with a deep association metric [Conference session]. 2017 Institute of Electrical and Electronics Engineers International Conference on Image Processing Beijing, China. https://doi.org/10.1109/ICIP.2017.8296962

[24] Can, V. X., Vu, P. X., Rui-fang, M., Thuat, V. T., Van Duy, V., & Noi, N. D. (2021). Vehicle detection and counting under mixed traffic conditions in vietnam using yolov4. International Journal of Advanced Research Engineering and Technology, 12(2), 722-730. https://doi.org/10.34218/IJARET.12.2.2021.072

[25] Mahto, P., Garg, P., Seth, P., & Panda, J. (2020). Refining yolov4 for vehicle detection. International Journal of Advanced Research in Engineering and Technology 11(5), 409-419. https://doi.org/10.34218/IJARET.11.5.2020.043

[26] Ho, N., Pham, M., Vo, N. D., & Nguyen, K. (2020, November 26-27). Vehicle Detection at Night Time [Conference session]. 7th National Foundation for Science and Technology Development Conference on Information and Computer Science Ho Chi Minh City, Vietnam. https://doi.org/10.1109/NICS51282.2020.9335870

[27] Hassaballah, M., Kenk, M. A., Muhammad, K., & Minaee, S. (2021). Vehicle Detection and Tracking in Adverse Weather Using a Deep Learning Framework. Institute of Electrical and Electronics Engineers Transactions on Intelligent Transportation Systems, 22(7), 4230-4242. https://doi.org/10.1109/TITS.2020.3014013

[28] Ni, Z., Liu, T., Li, K., Bai, Y., & Zhu, Z. (2021). Real-time vehicle detection and computer intelligent recognition through improved YOLOv4. Journal of Physics: Conference Series, 2083(4), 042006. https://doi.org/10.1088/1742-6596/2083/4/042006