CFFNet: Coordinated feature fusion network for crowd counting

Xia, Yinfeng; He, Yuqiang; Peng, Sifan; Yang, Qianqian; Yin, Baocai

doi:10.1016/j.imavis.2021.104242

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…Counting people in a crowd is a complex process; over the years, researchers tried to improve the proposed methods [ 57 ]. The development of new methods can be driven by the advantages and disadvantages of previous ones.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

Convolutional Neural Networks and Heuristic Methods for Crowd Counting: A Systematic Review

Hassen

Machado

Tavares

2022

Sensors

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The crowd counting task has become a pillar for crowd control as it provides information concerning the number of people in a scene. It is helpful in many scenarios such as video surveillance, public safety, and future event planning. To solve such tasks, researchers have proposed different solutions. In the beginning, researchers went with more traditional solutions, while recently the focus is on deep learning methods and, more specifically, on Convolutional Neural Networks (CNNs), because of their efficiency. This review explores these methods by focusing on their key differences, advantages, and disadvantages. We have systematically analyzed algorithms and works based on the different models suggested and the problems they are trying to solve. The main focus is on the shift made in the history of crowd counting methods, moving from the heuristic models to CNN models by identifying each category and discussing its different methods and architectures. After a deep study of the literature on crowd counting, the survey partitions current datasets into sparse and crowded ones. It discusses the reviewed methods by comparing their results on the different datasets. The findings suggest that the heuristic models could be even more effective than the CNN models in sparse scenarios.

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Section: Deep Learning Methodsmentioning

confidence: 99%

Convolutional Neural Networks and Heuristic Methods for Crowd Counting: A Systematic Review

Hassen

Machado

Tavares

2022

Sensors

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“…Xia et al . 26 proposed a coordinated feature fusion network (CFFNet) to solve the problem that spatial misalignment is ignored in feature extraction in traditional networks. A module called the spatial alignment module (SAM) was embedded to learn the offset of pixels to generate a high-quality density map for estimation and more detailed spatial distribution descriptions.…”

Section: Related Workmentioning

confidence: 99%

Offset-decoupled deformable convolution for efficient crowd counting

Zhong

Qin

Guo

et al. 2022

Sci Rep

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Crowd counting is considered a challenging issue in computer vision. One of the most critical challenges in crowd counting is considering the impact of scale variations. Compared with other methods, better performance is achieved with CNN-based methods. However, given the limit of fixed geometric structures, the head-scale features are not completely obtained. Deformable convolution with additional offsets is widely used in the fields of image classification and pattern recognition, as it can successfully exploit the potential of spatial information. However, owing to the randomly generated parameters of offsets in network initialization, the sampling points of the deformable convolution are disorderly stacked, weakening the effectiveness of feature extraction. To handle the invalid learning of offsets and the inefficient utilization of deformable convolution, an offset-decoupled deformable convolution (ODConv) is proposed in this paper. It can completely obtain information within the effective region of sampling points, leading to better performance. In extensive experiments, average MAE of 62.3, 8.3, 91.9, and 159.3 are achieved using our method on the ShanghaiTech A, ShanghaiTech B, UCF-QNRF, and UCF_CC_50 datasets, respectively, outperforming the state-of-the-art methods and validating the effectiveness of the proposed ODConv.

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CrowdAlign: Shared-weight dual-level alignment fusion for RGB-T crowd counting

Kong,

Yu,

et al. 2024

Image and Vision Computing

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CFFNet: Coordinated feature fusion network for crowd counting

Cited by 5 publications

References 16 publications

Convolutional Neural Networks and Heuristic Methods for Crowd Counting: A Systematic Review

Convolutional Neural Networks and Heuristic Methods for Crowd Counting: A Systematic Review

Offset-decoupled deformable convolution for efficient crowd counting

CrowdAlign: Shared-weight dual-level alignment fusion for RGB-T crowd counting

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