Computer Vision and Multi-Object Tracking for Traffic Measurement from Campus Monitoring Cameras
Publication: Computing in Civil Engineering 2021
ABSTRACT
Conventional methods of traffic monitoring primarily rely on sensors, on-board GPS, and human observation, which are generally resource-intensive, slow, and subject to implementation limits. Computer vision (particularly convolutional neural networks) is a more flexible, convenient, and cost-effective alternative to extract traffic information from traffic videos. This paper presents a methodology to extract traffic volume counts from cameras on Texas A&M University (TAMU) campus. Particularly, an object detector model, namely YOLOv5 (You Only Look Once), is integrated with an object tracking algorithm (Deep-SORT) to track vehicles in camera views. These tracks are projected using homography transformation from the local coordinate system of the camera onto an orthogonal map (world coordinates) along with information such as unique vehicle ID, type, position, and time stamp. This data is used to count vehicles that cross the pre-defined study zones (lanes) on different directions. A total of 60 samples (each 1 minute long) recorded during a 24-h period on TAMU campus are manually annotated and used to benchmark model predictions, resulting in a traffic volume count accuracy ranging from 68% to 93%. Moreover, the best input pixel resolution is determined to be 1,280 × 1,280 by measuring the average precision for each class of interest.
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REFERENCES
Al-Ariny, Z., Abdelwahab, M. A., Fakhry, M., and Hasaneen, E. S. (2020). “An efficient vehicle counting method using mask r-cnn.” Proc., 2020 International Conference on Innovative Trends in Communication and Computer Engineering (ITCE), IEEE, Aswan, Egypt, 232–237.
Bewley, A., Ge, Z., Ott, L., Ramos, F., and Upcroft, B. (2016). “Simple online and realtime tracking.” Proc., 2016 IEEE International Conference on Image Processing (ICIP), IEEE, Phoenix, Arizona, USA, 3464–3468.
Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y. M. (2020). “Yolov4: Optimal speed and accuracy of object detection.”.
Buch, N., Velastin, S. A., and Orwell, J. (2011). “A review of computer vision techniques for the analysis of urban traffic.” IEEE Transactions on Intelligent Transportation Systems, IEEE, 12(3), 920–939.
Chauhan, M. S., Singh, A., Khemka, M., Prateek, A., and Sen, R. (2019). “Embedded CNN based vehicle classification and counting in non-laned road traffic.” Proc., Tenth International Conference on Information and Communication Technologies and Development, 1–11.
Jocher, G. (2020). “ultralytics/yolov5: v4.0 - nn.SiLU() activations, Weights & Biases logging, PyTorch Hub integration.” <https://github. com/ultralytics/yolov5> (May, 2020).
Khajeh, H. M., Talebpour, A., and Shakkottai, S. (2019). “Privacy risk of connected vehicles in relation to vehicle tracking when transmitting basic safety message type 1 data.” Transportation research record, 2673(12), 636–643.
Leduc, G. (2008). “Road traffic data: Collection methods and applications.” Working Papers on Energy, Transport and Climate Change, European Commission Joint Research Centre Institute for Prospective, 1(55), 1–55.
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C. L. (2014). “Microsoft coco: Common objects in context.” Proc., European conference on computer vision, Springer, 740–755.
Liu, F., Zeng, Z., and Jiang, R. (2017). “A video-based real-time adaptive vehicle-counting system for urban roads.” PloS one, Public Library of Science San Francisco, CA USA, 12(11), e0186098.
Liu, X., Liu, W., Mei, T., and Ma, H. (2016). “A deep learning-based approach to progressive vehicle re-identification for urban surveillance.” Proc., European conference on computer vision, Springer, 869–884.
Milan, A., Leal-Taixé, L., Reid, I., Roth, S., and Schindler, K. (2016). “MOT16: A benchmark for multi-object tracking.”.
Naphade, M., Wang, S., Anastasiu, D. C., Tang, Z., Chang, M.-C., Yang, X., Zheng, L., Sharma, A., Chellappa, R., and Chakraborty, P. (2020). “The 4th AI city challenge.” Proc., IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 626–627.
Pi, Y. L., Nath, N. D., and Behzadan, A. H. (2020). “Convolutional neural networks for object detection in aerial imagery for disaster response and recovery.” Advanced Engineering Informatics, 43, 101009.
Pi, Y., Nath, N. D., and Behzadan, A. H. (2021). “Detection and Semantic Segmentation of Disaster Damage in UAV Footage.” Journal of Computing in Civil Engineering, American Society of Civil Engineers, 35(2), 4020063.
Seenouvong, N., Watchareeruetai, U., Nuthong, C., Khongsomboon, K., and Ohnishi, N. (2016). “A computer vision based vehicle detection and counting system.” Proc., 8th International Conference on Knowledge and Smart Technology (KST), IEEE, 224–227.
Shine, L., Edison, A., and Jiji, C. V. (2019). “A comparative study of faster r-cnn models for anomaly detection in 2019 ai city challenge.” Proc., IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 306–314.
Xiang, X., Zhai, M., Lv, N., and El Saddik, A. (2018). “Vehicle counting based on vehicle detection and tracking from aerial videos.” Sensors, Multidisciplinary Digital Publishing Institute, 18(8), 2560.
Zhang, Z., Liu, K., Gao, F., Li, X., and Wang, G. (2016). “Vision-based vehicle detecting and counting for traffic flow analysis.” Proc., International Joint Conference on Neural Networks (IJCNN), IEEE, 2267–2273.
Zhou, H., Wang, Y., Lei, X., and Liu, Y. (2017). “A method of improved CNN traffic classification.” Proc., 13th International Conference on Computational Intelligence and Security (CIS), IEEE, 177–181.
Zhou, J., Gao, D., and Zhang, D. (2007). “Moving vehicle detection for automatic traffic monitoring.” Transactions on vehicular technology, IEEE, 56(1), 51–59.
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Computing in Civil Engineering 2021
Pages: 950 - 958
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Published online: May 24, 2022
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