Abstract
The acceleration of urbanization has led to increasingly prominent traffic problems. In the context of the intelligent era, road traffic management is in urgent need of transformation and upgrading. The development of intelligent transportation is an important task for the construction of a modern city. The intelligent transportation system has the ability to automatically sense and analyze road conditions, which provides great convenience for traffic operation and management. Based on computer vision and deep learning technology, this paper designs and implements an intelligent traffic scene recognition system. The system processes the images taken by the traffic surveillance camera, and mainly realizes the intelligent recognition of two complex traffic scenes. One is the traffic statistics of passing vehicles at the intersection, and the other is vehicle speeding detection. The main process of system realization is divided into five steps. First, the YOLOv4 target detection algorithm is used to detect the vehicle. Second, use the SORT algorithm to track vehicles in real time, and then use the vector product-based virtual line counting method to achieve traffic flow statistics. Third, adopt the HyperLPR Chinese license plate recognition framework based on deep learning to recognize a wide range of license plates with high accuracy. Fourth, a two-line speed measurement method based on computer vision is designed to realize vehicle speeding detection. Finally, PyQT5 and WEB technology are used to realize the visualization of traffic recognition data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Qiu, L., Zhang, D., Tian, Y., Al-Nabhan, N.: Deep learning-based algorithm for vehicle detection in intelligent transportation systems. J. Supercomput. 77(10), 11083–11098 (2021). https://doi.org/10.1007/s11227-021-03712-9
Santos, M., Bastos-Filho, C.J.A., Maciel, A.M.A., Lima, E.: Counting vehicle with high-precision in Brazilian roads using YOLOv3 and deep SORT. In: 2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 69–76 (2020)
Zhang, W., Mao, Y., Han, Y.: SLPNet: towards end-to-end car license plate detection and recognition using lightweight CNN. In: Peng, Y., et al. (eds.) PRCV 2020. LNCS, vol. 12306, pp. 290–302. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60639-8_25
Du, S., Zhang, P., Zhang, B., et al.: Weak and occluded vehicle detection in complex infrared environment based on improved YOLOv4. IEEE Access 9, 25671–25680 (2021)
Kumar, S., Vishal, Sharma, P., Pal, N.: Object tracking and counting in a zone using YOLOv4, DeepSORT and TensorFlow. In: 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), pp. 1017–1022 (2021)
Wojke, N., Bewley, A., Paulus, D.: Simple online and realtime tracking with a deep association metric, pp. 3645–3649. IEEE (2017)
Chen, C., Liu, B., Wan, S., et al.: An edge traffic flow detection scheme based on deep learning in an intelligent transportation system. IEEE Trans. Intell. Transp. Syst. 22, 1840–1852 (2020)
Zha, M., Meng, G., Lin, C., Zhou, Z., Chen, K.: RoLMA: a practical adversarial attack against deep learning-based LPR systems. In: Liu, Z., Yung, M. (eds.) Inscrypt 2019. LNCS, vol. 12020, pp. 101–117. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-42921-8_6
Li, Y., Chen, S., Liu, P., et al.: A recognition algorithm suitable for complex scenes and various types of license plates. In: Proceedings of the 2020 9th International Conference on Computing and Pattern Recognition, pp. 50–55 (2020)
Bhardwaj, R., Dhull, A., Sharma, M.: A computationally efficient real-time vehicle and speed detection system for video traffic surveillance. In: Bansal, P., Tushir, M., Balas, V.E., Srivastava, R. (eds.) Proceedings of International Conference on Artificial Intelligence and Applications: ICAIA 2020, vol. 1164, pp. 583–594. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-4992-2_55
Kujawski, A., Dudek, T.: Analysis and visualization of data obtained from camera mounted on unmanned aerial vehicle used in areas of urban transport. Sustain. Cities Soc. 72, 103004 (2021)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Lei, X., Li, R., Lin, K. (2021). Application of Intelligent Traffic Scene Recognition Based on Computer Vision. In: Cui, L., Xie, X. (eds) Wireless Sensor Networks. CWSN 2021. Communications in Computer and Information Science, vol 1509. Springer, Singapore. https://doi.org/10.1007/978-981-16-8174-5_8
Download citation
DOI: https://doi.org/10.1007/978-981-16-8174-5_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-8173-8
Online ISBN: 978-981-16-8174-5
eBook Packages: Computer ScienceComputer Science (R0)
