設計與實作基於深度學習多目標物體追蹤與互動技術之狗追人車事件偵測

Design and Implementation of Deep Learning Multi-Object Tracking and Interaction Technologies for Detection of Human and Vehicle Chased by Dog

吳允誠(Yun-Cheng Wu)

指導教授 ： 黃仁俊

淡江大學/工學院/資訊工程學系碩士班/碩士(2022年)

https://doi.org/10.6846/TKU.2022.00198

摘要

本篇摘要未授權

關鍵字

影像處理 ； 物體追蹤 ； 物體互動 ； 目標配對 ； YOLO ； DeepSort ； LSTM

並列摘要

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並列關鍵字

Image Processing ； Object Tracking ； Object Interaction ； Target Matching ； YOLO ； Deepsort ； LSTM

參考文獻

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[2] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You only look once: Unified, real-time object detection," in Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, NV, USA, June. 2016, pp. 779-788.

[3] J. Redmon and A. Farhadi, "YOLO9000: better, faster, stronger," in Proceedings of the IEEE conference on computer vision and pattern recognition, Honolulu, HI, USA, July. 2017, pp. 7263-7271.

[4] J. Redmon and A. J. a. p. a. Farhadi, "Yolov3: An incremental improvement," arXiv preprint arXiv:1804.02767, 2018.

[5] A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. J. a. p. a. Liao, "Yolov4: Optimal speed and accuracy of object detection," arXiv preprint arXiv:2004.10934, 2020.