Transformer Networks for Future Person Localization in First-Person Videos

Amar Alikadic, Hideo Saito, Ryo Hachiuma

研究成果: Conference contribution


Reliably and accurately forecasting future trajectories of pedestrians is necessary for systems like autonomous vehicles or visual assistive devices to function correctly. While previous state-of-the-art methods relied on modeling social interactions with LSTMs, with videos captured with a static camera from a bird’s-eye view, our paper presents a new method that leverages the Transformers architecture and offers a reliable way to model future trajectories in first-person videos captured by a body-mounted camera, without having to model any social interactions. Accurately forecasting future trajectories is a challenging task, mainly due to how unpredictably humans move. We tackle this issue by using information about target persons’ previous locations, scales, and dynamic poses, as well as information about the camera wearer’s ego-motion. The model we propose predicts future trajectories in a simple way, modeling each target’s trajectory separately, without the use of complex social interactions between humans or interactions between targets and the scene. Experimental results show that our method overall outperforms previous state-of-the-art methods, and yields better results in challenging situations where previous state-of-the-art methods fail.

ホスト出版物のタイトルAdvances in Visual Computing - 17th International Symposium, ISVC 2022, Proceedings
編集者George Bebis, Bo Li, Angela Yao, Yang Liu, Ye Duan, Manfred Lau, Rajiv Khadka, Ana Crisan, Remco Chang
出版社Springer Science and Business Media Deutschland GmbH
出版ステータスPublished - 2022
イベント17th International Symposium on Visual Computing, ISVC 2022 - San Diego, United States
継続期間: 2022 10月 32022 10月 5


名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
13599 LNCS


Conference17th International Symposium on Visual Computing, ISVC 2022
国/地域United States
CitySan Diego

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)


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