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Delving Deep into Multiscale Pedestrian Detection via Single Scale Feature Maps

Fu, X; Yu, R; Zhang, W; Wu, J; Shao, S; (2018) Delving Deep into Multiscale Pedestrian Detection via Single Scale Feature Maps. Sensors , 18 (4) , Article 1063. 10.3390/s18041063. Green open access

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Abstract

The standard pipeline in pedestrian detection is sliding a pedestrian model on an image feature pyramid to detect pedestrians of different scales. In this pipeline, feature pyramid construction is time consuming and becomes the bottleneck for fast detection. Recently, a method called multiresolution filtered channels (MRFC) was proposed which only used single scale feature maps to achieve fast detection. However, there are two shortcomings in MRFC which limit its accuracy. One is that the receptive field correspondence in different scales is weak. Another is that the features used are not scale invariance. In this paper, two solutions are proposed to tackle with the two shortcomings respectively. Specifically, scale-aware pooling is proposed to make a better receptive field correspondence, and soft decision tree is proposed to relive scale variance problem. When coupled with efficient sliding window classification strategy, our detector achieves fast detecting speed at the same time with state-of-the-art accuracy

Type: Article
Title: Delving Deep into Multiscale Pedestrian Detection via Single Scale Feature Maps
Open access status: An open access version is available from UCL Discovery
DOI: 10.3390/s18041063
Publisher version: https://doi.org/10.3390/s18041063
Language: English
Additional information: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited https://creativecommons.org/licenses/by/4.0/
Keywords: pedestrian detection; boosted decision tree; scale invariance; receptive field correspondence; soft decision tree
UCL classification: UCL
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UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10092183
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