Abstract
Real-time awareness of the service status of facilities and equipment in metro tunnels is essential for the safe movement of trains. For low illumination underground tunnel environments where objects have high similarity characteristics and are difficult to distinguish, this paper proposes a framework for tracking high similarity objects along the rail line based on on-board visual perception devices. The framework mainly consists of two stages: object detection and object association. In the first stage, an object detection algorithm is used to detect potentially faulty objects in the video, and this paper takes the continuous occurrence of insulated steel supports on the line as the high similarity research object. In the second stage a matching strategy is used to associate the same object in the video sequence and assign the same ID (identity document). Finally, the proposed framework was tested on metro tunnel video and achieved an accuracy of 95.64 and a detection performance of 25 FPS (frames per second), 82.17 MOTA (multiple object tracking accuracy) and 88.46 MOTP (multiple object tracking precision) tracking performance.
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
Similar content being viewed by others
References
Zheng, J., Chen, Y., Zhang, H., Liu, D.: Foreign object detection algorithm based on multi-scale convolutional network. J. Phys.: Conf. Ser. 1952(2), 022017 (2021)
Chen, W., Meng, S., Jiang, Y.: Foreign object detection in railway images based on an efficient two-stage convolutional neural network. Comput. Intell. Neurosci. (2022)
Cao, Z., et al.: An effective railway intrusion detection method using dynamic intrusion region and lightweight neural network. Measurement 191, 110564 (2022)
Meng, C., Wang, Z., Shi, L., Gao, Y., Tao, Y., Wei, L.: SDRC-YOLO: a novel foreign object intrusion detection algorithm in railway scenarios. Electronics 12(5), 1256 (2023)
Li, Y., Qin, Y., **e, Z., Cao, Z., Jia, L., Yu, Z., Zheng, J., Zhang, E.: Efficient SSD: a real-time intrusion object detection algorithm for railway surveillance. In: International conference on sensing, diagnostics, prognostics, and control, pp. 391–395. IEEE Bei**g (2020)
Wang, H., Pei, H., Zhang, J.: Detection of locomotive signal lights and pedestrians on railway tracks using improved YOLOv4. IEEE Access 10, 15495–15505 (2022)
Wei, X., Yang, Z., Liu, Y., Wei, D., Jia, L., Li, Y.: Railway track fastener defect detection based on image processing and deep learning techniques: a comparative study. Eng. Appl. Artif. Intell. 80, 66–81 (2019)
Chandran, P., Asber, J., Thiery, F., Odelius, J., Rantatalo, M.: An investigation of railway fastener detection using image processing and augmented deep learning. Sustainability 13(21), 12051 (2021)
Wang, T., Yang, F., Tsui, K.: Real-time detection of railway track component via one-stage deep learning networks. Sensors 20(15), 4325(2021)
Feng, J., Yuan, H., Hu, Y., Lin, J., Liu, S., Luo, X.: Research on deep learning method for rail surface defect detection. IET Electr. Syst. Transp. 10(4), 436–442 (2020)
Wang, H., Li, M., Wan, Z.: Rail surface defect detection based on improved Mask R-CNN. Comput. Electr. Eng. 102, 108269 (2022)
Cao, Z., et al.: Haze removal of railway monitoring images using multi-scale residual network. IEEE Trans. Intell. Transp. Syst. 22(12), 7460–7473 (2020)
Cai, N, Chen, H, Li, Y, Peng, Y.: Intrusion detection and tracking at railway crossing. In: Proceedings of the 2019 International Conference on Artificial Intelligence and Advanced Manufacturing, pp. 1–6. (2019)
Qu, Z., Yi, W., Zhou, R., Wang, H., Chi, R.: Scale self-adaption tracking method of defog-PSA-Kcf defogging and dimensionality reduction of foreign matter intrusion along railway lines. IEEE Access 7, 126720–126733 (2019)
Qin, Y., Cao, Z., Sun, Y., Kou, L., Zhao, X., Wu, Y., Jia, L.: Research on Active Safety Methodologies for Intelligent Railway Systems. Engineering (2022)
yolov5, https://github.com/ultralytics/yolov5. Last accessed 17 Nov 2022
Bernardin, K., Stiefelhagen, R.: Evaluating multiple object tracking performance: the clear mot metrics. EURASIP J. Image Video Process. 2008, 1–10 (2008)
Wojke, N., Bewley, A., Paulus, D.: Simple online and realtime tracking with a deep association metric. In: International Conference on Image Processing, pp. 3645–3649. IEEE (2017)
Acknowledgments
This work is supported by the National Key R&D Program of China (Contract No. 2022YFB4300601).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 Bei**g Paike Culture Commu. Co., Ltd.
About this paper
Cite this paper
Lian, L. et al. (2024). A Railway Similarity Multiple Object Tracking Framework Based on Vehicle Front Video. In: Gong, M., Jia, L., Qin, Y., Yang, J., Liu, Z., An, M. (eds) Proceedings of the 6th International Conference on Electrical Engineering and Information Technologies for Rail Transportation (EITRT) 2023. EITRT 2023. Lecture Notes in Electrical Engineering, vol 1138. Springer, Singapore. https://doi.org/10.1007/978-981-99-9319-2_9
Download citation
DOI: https://doi.org/10.1007/978-981-99-9319-2_9
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-9318-5
Online ISBN: 978-981-99-9319-2
eBook Packages: EngineeringEngineering (R0)