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Original Article
An Onboard Multi-Sensor Fusion System for Real-Time Passenger Occupancy and Crowd State Detection in Railway Coaches
Karthika R1
Arun Prasad M2
Mahilnan M3
Sivasankaramoorthy M4
Jiregna Soressa Tolera5
1 Assistant professor, Department of Information Technology, Rathinam Technical Campus, Coimbatore, Tamil Nadu, India. 2 3 4 5 Department of Information Technology, Rathinam Technical Campus, Coimbatore, Tamil Nadu, India.
Published Online: March-April 2026
Pages: 116-119
Cite this article
↗ https://www.doi.org/10.59256/ijire.20260702016
References
1. C. Zhang, H. Li, X. Wang, and X. Yang, “Cross-scene crowd counting via deep convolutional neural networks,” Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2015.
2. Y. Li, X. Zhang, and D. Chen, “CSRNet: Dilated convolutional neural networks for understanding highly congested scenes,” Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2018.
3. V. A. Sindagi and V. M. Patel, “A survey of recent advances in CNN-based single image crowd counting and density estimation,” Pattern Recognition Letters, vol. 107, pp. 3–16, 2018.
4. S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137–1149, 2017.
5. J. Redmon and A. Farhadi, “YOLOv3: An incremental improvement,” arXiv preprint arXiv: 1804.02767, 2018.
6. N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2005.
7. H. Idrees, I. Saleemi, C. Seibert, and M. Shah, “Multi-source multi-scale counting in extremely dense crowd images,” Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2013.
8. S. Chen, D. Zhang, and L. O’Neill, “Sensor fusion for people counting using video and infrared sensors,” IEEE Sensors Journal, vol. 12, no. 5, pp. 1626–1635, 2012.
9. A. Alahi, V. Ramanathan, and L. Fei-Fei, “Social LSTM: Human trajectory prediction in crowded spaces,” Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2016.
10. W. Shi et al., “Edge computing: Vision and challenges,” IEEE Internet of Things Journal, vol. 3, no. 5, pp. 637–646, 2016.
11. M. Satyanarayanan, “The emergence of edge computing,” Computer, vol. 50, no. 1, pp. 30–39, 2017.
2. Y. Li, X. Zhang, and D. Chen, “CSRNet: Dilated convolutional neural networks for understanding highly congested scenes,” Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2018.
3. V. A. Sindagi and V. M. Patel, “A survey of recent advances in CNN-based single image crowd counting and density estimation,” Pattern Recognition Letters, vol. 107, pp. 3–16, 2018.
4. S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137–1149, 2017.
5. J. Redmon and A. Farhadi, “YOLOv3: An incremental improvement,” arXiv preprint arXiv: 1804.02767, 2018.
6. N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2005.
7. H. Idrees, I. Saleemi, C. Seibert, and M. Shah, “Multi-source multi-scale counting in extremely dense crowd images,” Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2013.
8. S. Chen, D. Zhang, and L. O’Neill, “Sensor fusion for people counting using video and infrared sensors,” IEEE Sensors Journal, vol. 12, no. 5, pp. 1626–1635, 2012.
9. A. Alahi, V. Ramanathan, and L. Fei-Fei, “Social LSTM: Human trajectory prediction in crowded spaces,” Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2016.
10. W. Shi et al., “Edge computing: Vision and challenges,” IEEE Internet of Things Journal, vol. 3, no. 5, pp. 637–646, 2016.
11. M. Satyanarayanan, “The emergence of edge computing,” Computer, vol. 50, no. 1, pp. 30–39, 2017.
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