MSF-ACNN: multi-scale feature fusion atrous convolutional neural networks for pedestrian fine-grained attribution detection

Zhenxia Yu; Miaomiao Lou; Lin Chen

doi:10.1117/12.2524259

6 May 2019 MSF-ACNN: multi-scale feature fusion atrous convolutional neural networks for pedestrian fine-grained attribution detection

Zhenxia Yu, Miaomiao Lou, Lin Chen

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Proceedings Volume 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018); 110690X (2019) https://doi.org/10.1117/12.2524259
Event: Tenth International Conference on Graphic and Image Processing (ICGIP 2018), 2018, Chengdu, China

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.

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Zhenxia Yu, Miaomiao Lou, and Lin Chen "MSF-ACNN: multi-scale feature fusion atrous convolutional neural networks for pedestrian fine-grained attribution detection ", Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110690X (6 May 2019); https://doi.org/10.1117/12.2524259

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KEYWORDS

Convolution

Convolutional neural networks

Head

Image resolution

Image fusion

Network architectures

Data modeling

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