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Proceedings Paper

MSF-ACNN: multi-scale feature fusion atrous convolutional neural networks for pedestrian fine-grained attribution detection
Author(s): Zhenxia Yu; Miaomiao Lou; Lin Chen
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Paper Abstract

Pedestrian fine-grained attribution detection has drawn significant interest in areas such as smart video surveillance analysis and pedestrian re-identification. Current object detection methods based on deep convolutional neural networks (CNNs) have achieved great progress. However, detections of small parts of pedestrian are still challenging due to their limited resolution and information in images. In this paper, we propose a novel CNN-based framework for improving the accuracy of small objects detection, dubbed MSF-ACNN, using atrous convolutions in cascade along with multi-scale feature fusion: 1) Atrous convolution effectively expands the field-of-view of small regions without increasing the number of parameters and computation. 2) Multi-scale feature fusion obtains more meaningful fine-grained information from both the low-level and high-level feature maps and can handle a variety of image scales. Our results show that MSF-ACNN can obtain better mean average precision (mAP) than the current state-of-the-art methods with faster detection speed, achieving significant improvements on certain small parts of pedestrian such as shoes, bag and hat.

Paper Details

Date Published: 6 May 2019
PDF: 8 pages
Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110690X (6 May 2019); doi: 10.1117/12.2524259
Show Author Affiliations
Zhenxia Yu, Chengdu Univ. of Information Technology (China)
Miaomiao Lou, Chengdu Univ. of Information Technology (China)
Lin Chen, Chongqing Institute of Green and Intelligent Technology (China)

Published in SPIE Proceedings Vol. 11069:
Tenth International Conference on Graphics and Image Processing (ICGIP 2018)
Chunming Li; Hui Yu; Zhigeng Pan; Yifei Pu, Editor(s)

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