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Crowd Counting via Segmentation Guided Attention Networks and Curriculum Loss

Wang, Q.; Breckon, T.P.

Crowd Counting via Segmentation Guided Attention Networks and Curriculum Loss Thumbnail


Q. Wang


Automatic crowd behaviour analysis is an important task for intelligent transportation systems to enable effective flow control and dynamic route planning for varying road participants. Crowd counting is one of the keys to automatic crowd behaviour analysis. Crowd counting using deep convolutional neural networks (CNN) has achieved encouraging progress in recent years. Researchers have devoted much effort to the design of variant CNN architectures and most of them are based on the pre-trained VGG16 model. Due to the insufficient expressive capacity, the backbone network of VGG16 is usually followed by another cumbersome network specially designed for good counting performance. Although VGG models have been outperformed by Inception models in image classification tasks, the existing crowd counting networks built with Inception modules still only have a small number of layers with basic types of Inception modules. To fill in this gap, in this paper, we firstly benchmark the baseline Inception-v3 model on commonly used crowd counting datasets and achieve surprisingly good performance comparable with or better than most existing crowd counting models. Subsequently, we push the boundary of this disruptive work further by proposing a Segmentation Guided Attention Network (SGANet) with Inception-v3 as the backbone and a novel curriculum loss for crowd counting. We conduct thorough experiments to compare the performance of our SGANet with prior arts and the proposed model can achieve state-of-the-art performance with MAE of 57.6, 6.3 and 87.6 on ShanghaiTechA, ShanghaiTechB and UCF_QNRF, respectively


Wang, Q., & Breckon, T. (2022). Crowd Counting via Segmentation Guided Attention Networks and Curriculum Loss. IEEE Transactions on Intelligent Transportation Systems,

Journal Article Type Article
Acceptance Date Dec 14, 2021
Publication Date 2022
Deposit Date Jan 5, 2022
Publicly Available Date Jan 5, 2022
Journal IEEE Transactions on Intelligent Transportation Systems
Print ISSN 1524-9050
Electronic ISSN 1558-0016
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Publisher URL


Accepted Journal Article (3.7 Mb)

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