@inproceedings{c4402140f7b947568df040acb3f4700c,
title = "Data Association with Graph Network for Multi-Object Tracking",
abstract = "Multi-Object Tracking (MOT) methods within Tracking-by-Detection paradigm are usually modeled as graph problem. It is challenging to associate objects in dense scenes with frequent occlusion. To further model object interactions and repair detection errors, we use graph network to extract embeddings for data association. Graph neural network makes it possible for embeddings aggregate and update between vertices (detections and trajectories). We both introduce priori confidence to detection attention and trajectory attention, which consider the interaction between occluded objects in the same frame. Based on MHT framework, we train two graph networks for clustering in adjacent frame and association between long spaced tracklets. Experiments on MOT17/20 benchmarks demonstrate the significant improving in tracking accuracy of proposed method and show state-of-the-art performance for MOT with public detections.",
keywords = "Data association, Graph neural network, Multiple object tracking",
author = "Yubin Wu and Hao Sheng and Shuai Wang and Yang Liu and Wei Ke and Zhang Xiong",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 15th International Conference on Knowledge Science, Engineering and Management, KSEM 2022 ; Conference date: 06-08-2022 Through 08-08-2022",
year = "2022",
doi = "10.1007/978-3-031-10983-6\_21",
language = "英语",
isbn = "9783031109829",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "268--280",
editor = "Gerard Memmi and Baijian Yang and Linghe Kong and Tianwei Zhang and Meikang Qiu",
booktitle = "Knowledge Science, Engineering and Management - 15th International Conference, KSEM 2022, Proceedings",
address = "德国",
}