@inproceedings{4f263d520fa64d56bacef05d6c882742,
title = "Research on Video Pedestrian Tracking Based on the Combination of Optical Flow Method and Target Tracking Network",
abstract = "Target tracking is designed to predict the position of a given target in each video frame, and it has a wide range of applications in the fields of robot vision, video surveillance, and unmanned driving. This paper mainly studies the challenges of interference and target occlusion of similarities in target tracking, takes pedestrians as the main research target, combines the optical flow algorithm with the target tracking network, and improves the optical flow algorithm and the target tracking model, thereby improving the robustness of the tracking algorithm. Extensive experiments and comparative tests show the effectiveness of our method.",
keywords = "Diffusion equation, Self-attention mechanism, Target tracking, Variational optical flow",
author = "Enzhe Zhao and Dazhi Zhang and Yao Li and Yuying Guo and Boying Wu and Zhichang Guo and Jie Ning",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.; 7th International Scientific Conference on Intelligent Information Technologies for Industry, IITI 2023 ; Conference date: 25-09-2023 Through 30-09-2023",
year = "2023",
doi = "10.1007/978-3-031-43789-2\_11",
language = "英语",
isbn = "9783031437885",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "120--135",
editor = "Sergey Kovalev and Andrey Sukhanov and Igor Kotenko",
booktitle = "Proceedings of the 7th International Scientific Conference on Intelligent Information Technologies for Industry (IITI{\textquoteright}23) - Volume 1",
address = "德国",
}