{"type":"ScholarlyArticle","title":"Grouping Attribute Recognition for Pedestrian with Joint Recurrent Learning","authors":[{"name":"Xin Zhao","slug":"xin-zhao","given":"Xin","family":"Zhao","affiliation":"Beijing National Research Center for Information Science and Technology(BNRist)"},{"name":"Liufang Sang","slug":"liufang-sang","given":"Liufang","family":"Sang","affiliation":"Beijing National Research Center for Information Science and Technology(BNRist)"},{"name":"Guiguang Ding","slug":"guiguang-ding","given":"Guiguang","family":"Ding","affiliation":"Beijing National Research Center for Information Science and Technology(BNRist); School of Software , Tsinghua University , Beijing 100084 , China"},{"name":"Yuchen Guo","slug":"yuchen-guo","given":"Yuchen","family":"Guo","affiliation":"Beijing National Research Center for Information Science and Technology(BNRist)"},{"name":"Xiaoming Jin","slug":"xiaoming-jin","given":"Xiaoming","family":"Jin","affiliation":"Beijing National Research Center for Information Science and Technology(BNRist)"}],"abstract":"Pedestrian attributes recognition is to predict attribute labels of pedestrian from surveillance images, which is a very challenging task for computer vision due to poor imaging quality and small training dataset.  It is observed that semantic pedestrian attributes to be recognised tend to show semantic or visual spatial correlation. Attributes can be grouped by the correlation while previous works mostly ignore this phenomenon. Inspired by Recurrent Neural Network (RNN)'s super capability of learning context correlations, this paper proposes an end-to-end Grouping Recurrent Learning (GRL) model that takes advantage of the intra-group mutual exclusion and inter-group correlation to improve the performance of pedestrian attribute recognition. Our GRL method starts with the detection of precise body region via Body Region Proposal followed by feature extraction from detected regions. These features, along with the semantic groups, are fed into RNN for recurrent grouping attribute recognition, where intra group correlations can be learned. Extensive empirical evidence shows that our GRL model achieves state-of-the-art results, based on pedestrian attribute datasets, i.e. standard PETA and RAP datasets.","doi":"10.24963/ijcai.2018/441","url":"https://ijcai.org/proceedings/2018/441","pdf":"https://ijcai.org/proceedings/2018/0441.pdf","markdown":"https://ijcai.org/proceedings/2018/441.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/441","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Machine Learning","track":"Main track","number":441,"pages":{"first":3177,"last":3183},"keywords":["Machine Learning: Neural Networks","Machine Learning: Deep Learning","Machine Learning: Multi-instance;Multi-label;Multi-view learning"],"videos":[],"references":[{"position":1,"raw":"[Andriluka et al., 2014] Mykhaylo Andriluka, Leonid Pishchulin, Peter V. 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