Abstract
Several traffic studies necessitate vehicle counting during peak hours and throughout the day, as well as detailed classification and tracking, which consumes human time and effort, particularly at intersections. Manual efforts primarily collect the necessary traffic demand data live in the field or from video recordings using an extensive data manipulation process. Alternative solutions include computer-based systems that perform human tasks more efficiently and with less time and effort, and these systems vary in function and performance. This paper proposes a comprehensive computer-based system that detects, tracks, and computes related statistics in real time while making the best use of available resources, such as public road surveillance cameras. The main contribution of this work is the effectiveness of combining various computer vision algorithms to achieve high-accuracy performance during real-time streaming of road cameras. The experiments confirm the system’s performance by achieving an average success rate of 93.2%. The novel aspect of this work is that detections, point extractions, matching, tracking, and classification were implemented in a single system that ensures real-time execution and high-accuracy output and uses existing infrastructure. The system compensates for the variations in light between day and night and between cloudy and sunny weather. It also recovers hidden vehicles and changes in view for each vehicle as it moves. The proposed method efficiently and partially integrates some mechanisms into a single system.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
References
Abdelwahab, M. A., and M. M. Abdelwahab. 2015. “A novel algorithm for vehicle detection and tracking in airborne videos.” In Proc., 2015 IEEE Int. Symp. on Multimedia (ISM), 65–68. New York: IEEE. https://doi.org/10.1109/ISM.2015.77.
Alan, L., T. Vojíř, L. Čehovin, J. Matas, and M. Kristan. 2018. “Discriminative correlation filter tracker with channel and spatial reliability.” Int. J. Comput. Vision 126 (7): 671–688. https://doi.org/10.1007/s11263-017-1061-3.
Al-Garni, S. M., and A. A. Abdennour. 2006. “Moving vehicles detection using automatic background extraction.” In Vol. 18 of Proc., World Academy of Science, Engineering and Technology, 180–184. Bingham Farms, MI: Seventh Sense Research Group.
Alomari, A. H., H. Al-Deek, A. Sandt, J. H. Rogers Jr., and O. Hussain. 2016. “Regional evaluation of bus rapid transit with and without transit signal priority.” Transp. Res. Rec. 2554 (1): 46–59. https://doi.org/10.3141/2554-06.
Bay, H., A. Ess, T. Tuytelaars, and L. Van Gool. 2008. “Speeded-up robust features (SURF).” Comput. Vision Image Understanding 110 (3): 346–359. https://doi.org/10.1016/j.cviu.2007.09.014.
Bay, H., T. Tuytelaars, and L. Van Gool. 2006. “SURF: Speeded up robust features.” In Proc., European Conf. on Computer Vision, 404–417. Berlin: Springer.
Betke, M., E. Haritaoglu, and L. S. Davis. 1996. “Multiple vehicle detection and tracking in hard real-time.” In Proc., Conf. on Intelligent Vehicles, 351–356. New York: IEEE. https://doi.org/10.1109/ivs.1996.566405.
Betke, M., E. Haritaoglu, and L. S. Davis. 2000. “Real-time multiple vehicle detection and tracking from a moving vehicle.” Mach. Vision Appl. 12 (2): 69–83. https://doi.org/10.1007/s001380050126.
Bugeja, M., A. Dingli, M. Attard, and D. Seychell. 2020. “Comparison of vehicle detection techniques applied to IP camera video feeds for use in intelligent transport systems.” Transp. Res. Procedia 45 (2020): 971–978. https://doi.org/10.1016/j.trpro.2020.02.069.
Chen, X., and Q. Meng. 2013. “Vehicle detection from UAVs by using SIFT with implicit shape model.” In Proc., 2013 IEEE Int. Conf. on Systems, Man, and Cybernetics, 3139–3144. New York: IEEE. https://doi.org/10.1109/SMC.2013.535.
Chen, X., and Q. Meng. 2015. “Robust vehicle tracking and detection from UAVs.” In Proc., 7th Int. Conf. of Soft Computing and Pattern Recognition (SoCPaR), 241–246. New York: IEEE. https://doi.org/10.1109/SOCPAR.2015.7492814.
Daigavane, P. M., P. R. Bajaj, and M. B. Daigavane. 2011. “Vehicle detection and neural network application for vehicle classification.” In Proc., 2011 Int. Conf. on Computational Intelligence and Communication Networks, 758–762. New York: IEEE. https://doi.org/10.1109/CICN.2011.168.
Dallalzadeh, E., and D. S. Guru. 2010. “Feature-based tracking approach for detection of moving vehicle in traffic videos.” In Proc., 1st Int. Conf. on Intelligent Interactive Technologies and Multimedia, 254–260. New York: Association for Computing Machinery. https://doi.org/10.1145/1963564.1963609.
Gomaa, A., M. M. Abdelwahab, M. Abo-Zahhad, T. Minematsu, and R. I. Taniguchi. 2019. “Robust vehicle detection and counting algorithm employing a convolution neural network and optical flow.” Sensors 19 (20): 4588. https://doi.org/10.3390/s19204588.
Li, X., X. Yao, Y. L. Murphey, R. Karlsen, and G. Gerhart. 2004. “A real-time vehicle detection and tracking system in outdoor traffic scenes.” In Vol. 2 of Proc., 17th Int. Conf. on Pattern Recognition, 2004, 761–764. New York: IEEE. https://doi.org/10.1109/icpr.2004.1334370.
Liu, Y., B. Tian, S. Chen, F. Zhu, and K. Wang. 2013. “A survey of vision-based vehicle detection and tracking techniques in ITS.” In Proc., 2013 IEEE Int. Conf. on Vehicular Electronics and Safety, 72–77. New York: IEEE. https://doi.org/10.1109/ICVES.2013.6619606.
Lowe, D. G. 2004. “Distinctive image features from scale-invariant keypoints.” Int. J. Comput. Vision 60 (2): 91–110. https://doi.org/10.1023/B:VISI.0000029664.99615.94.
Muja, M., and D. G. Lowe. 2009. “Fast approximate nearest neighbors with automatic algorithm configuration.” In Vol. 1 of Proc., Fourth Int. Conf. on Computer Vision Theory and Applications, 331–340. Delft, Netherlands: Science and Technology Publications. https://doi.org/10.5220/0001787803310340.
Pawar, B., V. T. Humbe, and L. Kundnani. 2017. “Morphology based moving vehicle detection.” In Proc., Int. Conf. on Big Data Analytics and Computational Intelligence (ICBDAC), 217–223. New York: IEEE. https://doi.org/10.1109/ICBDACI.2017.8070837.
Redmon, J., S. Divvala, R. Girshick, and A. Farhadi. 2016. “You only look once: Unified, real-time object detection.” In Proc., IEEE Conf. on Computer Vision and Pattern Recognition, 779–788. New York: IEEE. https://doi.org/10.1109/CVPR.2016.91.
Sheng, M., C. Liu, Q. Zhang, L. Lou, and Y. Zheng. 2018. “Vehicle detection and classification using convolutional neural networks.” In Proc., 7th Data Driven Control and Learning Systems Conf. (DDCLS), 581–587. New York: IEEE. https://doi.org/10.1109/DDCLS.2018.8516099.
Srinivasa, N. 2002. “Vision-based vehicle detection and tracking method for forward collision warning in automobiles.” In Vol. 2 of Proc., Intelligent Vehicle Symp., 626–631. New York: IEEE. https://doi.org/10.1109/ivs.2002.1188021.
Wen, L., D. Du, Z. Cai, Z. Lei, M. C. Chang, H. Qi, J. Lim, M. H. Yang, and S. Lyu. 2015. “UA-DETRAC: A new benchmark and protocol for multi-object detection and tracking.” Preprint, submitted November 13, 2015. http://arxiv.org/abs/1511.04136.
Zhao, T., and R. Nevatia. 2003. “Car detection in low resolution aerial images.” Image Vision Comput. 21 (8): 693–703. https://doi.org/10.1016/S0262-8856(03)00064-7.
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© 2022 American Society of Civil Engineers.
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Received: Oct 16, 2021
Accepted: May 12, 2022
Published online: Jul 20, 2022
Published in print: Oct 1, 2022
Discussion open until: Dec 20, 2022
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