Object Detection Through Computer Vision
Author:
Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-14859-0_11
Reference17 articles.
1. Asha, C., Narasimhadhan, A.: Vehicle counting for traffic management system using yolo and correlation filter. In: 2018 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), pp. 1–6. IEEE (2018)
2. Bedruz, R.A., Sybingco, E., Bandala, A., Quiros, A.R., Uy, A.C., Dadios, E.: Real-time vehicle detection and tracking using a mean-shift based blob analysis and tracking approach. In: 2017IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), pp. 1–5 (2017). https://doi.org/10.1109/HNICEM.2017.8269528
3. Bertozzi, M., Broggi, A., Cellario, M., Fascioli, A., Lombardi, P., Porta, M.: Artificial vision in road vehicles. Proc. IEEE 90(7), 1258–1271 (2002)
4. Bielli, M., Ambrosino, G., Boero, M., Mastretta, M.: Artificial intelligence techniques for urban traffic control. Transp. Res. Part A: Gener. 25(5), 319–325 (1991)
5. Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: YOLOv4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)
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