MASK GUIDED SPATIAL-TEMPORAL FUSION NETWORK FOR MULTIPLE OBJECT TRACKING

Shuangye Zhao, Yubin Wu, Shuai Wang, Wei Ke, Hao Sheng

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

Multi-object trackers make the association almost perfectly when no occlusion occurred between two or more targets. However, it is hard to extract reliable features on account of partial occlusion caused by a nearby object, which often leads to tracking failure. In this paper, we utilize mask to guide attention of the neural network in order to focus on the visible part of the target and design a tracklet-level feature extraction method. Then, a tracking framework is proposed based on a mask guided fusion network and multi-hypothesis tracking algorithm. Comprehensive evaluation on the MOT17 dataset shows that our approach achieves competitive results.

原文English
主出版物標題2022 IEEE International Conference on Image Processing, ICIP 2022 - Proceedings
發行者IEEE Computer Society
頁面3231-3235
頁數5
ISBN(電子)9781665496209
DOIs
出版狀態Published - 2022
事件29th IEEE International Conference on Image Processing, ICIP 2022 - Bordeaux, France
持續時間: 16 10月 202219 10月 2022

出版系列

名字Proceedings - International Conference on Image Processing, ICIP
ISSN(列印)1522-4880

Conference

Conference29th IEEE International Conference on Image Processing, ICIP 2022
國家/地區France
城市Bordeaux
期間16/10/2219/10/22

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