Leveraging Heterogeneous Auxiliary Tasks to Assist Crowd Counting

Zhao, Muming; Zhang, Jian; Zhang, Chongyang; Zhang, Wenjun

doi:10.1109/cvpr.2019.01302

Cited by 140 publications

(67 citation statements)

References 21 publications

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“…Furthermore, to alleviate the effects of background objects for foreground crowd counting, foreground mask-based crowd counting networks [ 50 , 51 , 52 , 53 ] have been designed. Although the above methods achieved promising results, they rely on training data, and therefore their generalization ability is limited to new scenarios.…”

Section: Related Workmentioning

confidence: 99%

Congested Crowd Counting via Adaptive Multi-Scale Context Learning

Zhang

Zhao

Duan

et al. 2021

Sensors

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In this paper, we propose a novel congested crowd counting network for crowd density estimation, i.e., the Adaptive Multi-scale Context Aggregation Network (MSCANet). MSCANet efficiently leverages the spatial context information to accomplish crowd density estimation in a complicated crowd scene. To achieve this, a multi-scale context learning block, called the Multi-scale Context Aggregation module (MSCA), is proposed to first extract different scale information and then adaptively aggregate it to capture the full scale of the crowd. Employing multiple MSCAs in a cascaded manner, the MSCANet can deeply utilize the spatial context information and modulate preliminary features into more distinguishing and scale-sensitive features, which are finally applied to a 1 × 1 convolution operation to obtain the crowd density results. Extensive experiments on three challenging crowd counting benchmarks showed that our model yielded compelling performance against the other state-of-the-art methods. To thoroughly prove the generality of MSCANet, we extend our method to two relevant tasks: crowd localization and remote sensing object counting. The extension experiment results also confirmed the effectiveness of MSCANet.

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Section: Related Workmentioning

confidence: 99%

Congested Crowd Counting via Adaptive Multi-Scale Context Learning

Zhang

Zhao

Duan

et al. 2021

Sensors

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“…One major reason is the lack of crowd localization information. Some recent studies (Zhao et al 2019;Liu, Weng, and Mu 2019) have tried to exploit the useful information from localization in a unified framework. They, however, only simply share the underlying representations or interweave two modules for different task together for more robust representations.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Graph Neural Networks for Crowd Counting

Luo

Yang

et al. 2020

AAAI

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Crowd counting is an important yet challenging task due to the large scale and density variation. Recent investigations have shown that distilling rich relations among multi-scale features and exploiting useful information from the auxiliary task, i.e., localization, are vital for this task. Nevertheless, how to comprehensively leverage these relations within a unified network architecture is still a challenging problem. In this paper, we present a novel network structure called Hybrid Graph Neural Network (HyGnn) which targets to relieve the problem by interweaving the multi-scale features for crowd density as well as its auxiliary task (localization) together and performing joint reasoning over a graph. Specifically, HyGnn integrates a hybrid graph to jointly represent the task-specific feature maps of different scales as nodes, and two types of relations as edges: (i) multi-scale relations capturing the feature dependencies across scales and (ii) mutual beneficial relations building bridges for the cooperation between counting and localization. Thus, through message passing, HyGnn can capture and distill richer relations between nodes to obtain more powerful representations, providing robust and accurate results. Our HyGnn performs significantly well on four challenging datasets: ShanghaiTech Part A, ShanghaiTech Part B, UCF_CC_50 and UCF_QNRF, outperforming the state-of-the-art algorithms by a large margin.

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“…Zhang and Chang [17] proposed a novel network, which could combine the information of multiple cameras to estimate the crowd density of the scene. Zhao et al [18] proposed a new crowd counting method that could use heterogeneous attributes to solve the problem of various scales, complex backgrounds, and occlusion. Jiang et al [19] proposed Trellis Encoder-Decoder Networks (TEDnet), which could estimate a high-quality crowd density map for counting.…”

Section: Related Work a Crowd Countingmentioning

confidence: 99%

HAGN: Hierarchical Attention Guided Network for Crowd Counting

2020

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In recent years, deep learning based crowd counting networks have achieved significant progress. However, most of them generate rough crowd density maps due to low-resolution features used for estimating crowd distribution, which affects the performance of crowd counting. To solve this problem, in this paper, we propose a Hierarchical Attention Guided Network (HAGN) for crowd counting. We apply the first 13 layers of VGG-16 to extract base features. Then, the extracted features are processed by the Hierarchical Attention Mechanism (HAM), which guided the extracted features to enlarge step by step via our proposed attention guided branch. Finally, the outputs of HAM are fed to 1 × 1 convolutional layer for final crowd density estimation. Experiments are performed on ShanghaiTech and UCF-QNRF datasets, and our HAGN achieves promising performance compared with the other state-of-the-art methods on crowd counting and crowd localization, respectively.

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Leveraging Heterogeneous Auxiliary Tasks to Assist Crowd Counting

Cited by 140 publications

References 21 publications

Congested Crowd Counting via Adaptive Multi-Scale Context Learning

Congested Crowd Counting via Adaptive Multi-Scale Context Learning

Hybrid Graph Neural Networks for Crowd Counting

HAGN: Hierarchical Attention Guided Network for Crowd Counting

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