TransCenter: Transformers With Dense Representations for Multiple-Object Tracking-Reference-Cited by-同舟云学术

TransCenter: Transformers With Dense Representations for Multiple-Object Tracking

Author:

Xu Yihong¹^ORCID,Ban Yutong²^ORCID,Delorme Guillaume³,Gan Chuang⁴^ORCID,Rus Daniela²^ORCID,Alameda-Pineda Xavier³^ORCID

Affiliation:

1. Inria and now with Valeo.ai, Paris, France

2. Distributed Robotics Lab, CSAIL, Massachusetts Institute of Technology, Cambridge, MA, USA

3. Inria Grenoble Rhône-Alpes, Montbonnot-Saint-Martin, France

4. MIT-IBM Watson AI Lab, Cambridge, MA, USA

Funder

ANR ML3RI Project

H2020 SPRING Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Vision and Pattern Recognition,Software

Link

Reference96 articles.

1. An image is worth 16x16 words: Transformers for image recognition at scale;dosovitskiy,2020

4. Performance measures and a data set for multi-target, multi-camera tracking;ristani;Proc Eur Conf Comput Vis,2016

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