@inproceedings{e673d95e78e946259c117f6a5fdf86bd,
title = "Automatic Tracking and Human Identification Based on Combination Between YOLO and KCF Algorithm",
abstract = "This research introduces a hybrid tracking system that integrates the YOLO (You Only Look Once) and KCF (Kernelized Correlation Filter) algorithms for enhanced automatic tracking and human identification. The study, conducted at Beihang University, addresses the limitations of the KCF algorithm in handling device offset and target loss by utilizing YOLO for initialization and re-calibration. The Offset Error Rate (OER) serves as a metric to decide when YOLO should update the KCF tracking frame. The proposed method demonstrates improved tracking accuracy and robustness, with potential applications in autonomous navigation and obstacle avoidance for unmanned vehicles. Future work will focus on optimizing the YOLO structure and KCF algorithm for higher efficiency. The research is supported by various foundations, including the Chinese National Natural Science Foundation.",
keywords = "Air-ground coordination, autonomous navigation and obstacle avoidance, target tracking",
author = "Wei Lan and Yuangan Li and Zhenning Hu and Donghao Wang and Yang Du and Yangzhu Wang and Ke Li",
note = "Publisher Copyright: {\textcopyright} Beijing KeCui Man-Machine-Environment System Engineering Technology Research Academy 2024.; 24th Conference on Man-Machine-Environment System Engineering, MMESE 2024 ; Conference date: 18-10-2024 Through 20-10-2024",
year = "2024",
doi = "10.1007/978-981-97-7139-4\_75",
language = "英语",
isbn = "9789819771387",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "540--547",
editor = "Shengzhao Long and Dhillon, \{Balbir S.\} and Long Ye",
booktitle = "Man-Machine-Environment System Engineering - Proceedings of the 24th Conference on MMESE",
address = "德国",
}