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Robust Pedestrian Detection for Semi-automatic Construction of A Crowded Person Re-Identification Dataset

Huang, Z, Feng, Zhenhua, Yan, Fei, Kittler, Josef and Wu, X-J (2018) Robust Pedestrian Detection for Semi-automatic Construction of A Crowded Person Re-Identification Dataset In: X Conference on Articulated Motion and Deformable Objects 2018, 12 - 13 July 2018, Palma de Mallorca, Spain.

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Abstract

The problem of re-identification of people in a crowd com- monly arises in real application scenarios, yet it has received less atten- tion than it deserves. To facilitate research focusing on this problem, we have embarked on constructing a new person re-identification dataset with many instances of crowded indoor and outdoor scenes. This paper proposes a two-stage robust method for pedestrian detection in a complex crowded background to provide bounding box annotations. The first stage is to generate pedestrian proposals using Faster R-CNN and locate each pedestrian using Non-maximum Suppression (NMS). Candidates in dense proposal regions are merged to identify crowd patches. We then apply a bottom-up human pose estimation method to detect individual pedestrians in the crowd patches. The locations of all subjects are achieved based on the bounding boxes from the two stages. The identity of the detected subjects throughout each video is then automatically annotated using multiple features and spatial-temporal clues. The experimental results on a crowded pedestrians dataset demonstrate the effectiveness and efficiency of the proposed method.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Huang, Z
Feng, Zhenhuaz.feng@surrey.ac.uk
Yan, FeiF.Yan@surrey.ac.uk
Kittler, JosefJ.Kittler@surrey.ac.uk
Wu, X-J
Date : 17 June 2018
Funders : EPSRC
DOI : 10.1007/978-3-319-94544-6_7
Copyright Disclaimer : The final authenticated version is available online at https://doi.org/10.1007/978-3-319-94544-6_7
Uncontrolled Keywords : Person Re-identification; Pedestrian Detection; Faster R- CNN; Human Pose Estimation.
Related URLs :
Depositing User : Melanie Hughes
Date Deposited : 05 Jun 2018 10:27
Last Modified : 11 Dec 2018 11:24
URI: http://epubs.surrey.ac.uk/id/eprint/846990

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