Multiple object tracking based on multi‐task learning with strip attention
Abstract Multiple object tracking (MOT) framework based on bifurcate strategy was usually challenged by data association of different model path, which work for object localisation and appearance embedding independently. By incorporating the re‐identification (re‐ID) as appearance embedding model, m...
Enregistré dans:
Auteurs principaux: | Yaoye Song, Peng Zhang, Wei Huang, Yufei Zha, Tao You, Yanning Zhang |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Wiley
2021
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/a098874d1fbd491b82c79120db0433c9 |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Learn from Object Counting: Crowd Counting with Meta‐learning
par: Changtong Zan, et autres
Publié: (2021) -
Multi‐dimensional weighted cross‐attention network in crowded scenes
par: Yefan Xie, et autres
Publié: (2021) -
Part‐level attention networks for cross‐domain person re‐identification
par: Qun Zhao, et autres
Publié: (2021) -
CA‐PMG: Channel attention and progressive multi‐granularity training network for fine‐grained visual classification
par: Peipei Zhao, et autres
Publié: (2021) -
MFP‐Net: Multi‐scale feature pyramid network for crowd counting
par: Tao Lei, et autres
Publié: (2021)