Calibration-Free Multi-view Crowd Counting

Zhang, Qi; Chan, Antoni B.

doi:10.1007/978-3-031-20077-9_14

Cited by 8 publications

(3 citation statements)

References 47 publications

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“…Zhang et al [33] proposed a cross-view cross-scene multi-view counting model (CSCV) that incorporates camera selection and noise into the training and can output density maps in diferent scenes with arbitrary camera layouts. In [34], a multi-view counting model without calibration (CF-MVCC) was proposed to obtain the whole scene person by weighting the confdence score of camera view content and distance information. To solve the view scale inconsistency problem in multi-view counting methods, Liu et al [35] proposed a multiview crowd counting model (SASNet) based on scale aggregation and spatially aware networks, in which a multi-branch adaptive scale aggregation module selects the appropriate scale for each pixel in each view based on the extracted features, which can ensure the scale consistency across views.…”

Section: Multi-view Perception Methodsmentioning

confidence: 99%

A Fusion-Based Dense Crowd Counting Method for Multi-Imaging Systems

Zhang,

Ye,

et al. 2023

International Journal of Intelligent Systems

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Dense crowd counting has become an essential technology for urban security management. The traditional crowd counting methods mainly apply to the scene with a single view and obvious features but cannot solve the problem with a large area and fuzzy crowd features. Therefore, this paper proposes a crowd counting method based on high and low view information fusion (HLIF) for large and complex scenes. First, a neural network based on an attention mechanism (AMNet) is established to obtain a global density map from a high view and crowd counts from a low view. Then, the temporal correlation and spatial complementarity between cameras are used to calibrate the overlap areas of the two images. Finally, the total number of people is calculated by combining the low-view crowd counts and the high-view density map. Compared to single-view crowd counting methods, HLIF is experimentally more accurate and has been successfully applied in practice.

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Section: Multi-view Perception Methodsmentioning

confidence: 99%

A Fusion-Based Dense Crowd Counting Method for Multi-Imaging Systems

Zhang,

Ye,

et al. 2023

International Journal of Intelligent Systems

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“…Wang et al [21] proposed a single-column scale-invariant network (ScSiNet), which extracts complex scale-invariant features by combining inter-layer multiscale integration and a new intra-layer scale-invariant transformation (SiT). Zhang et al [22] proposed a calibration-free multi-view crowd counting (CF-MVCC) method, which can directly obtain scene level counts from the density map prediction of each camera. Olmschenk [23] proposed a Semi-Supervised Dual-Goal Generative Adversarial Networks, which allows Dual-Goal GAN to benefit from unlabelled data during training and improves the prediction ability of the network.…”

Section: Crowd Countingmentioning

confidence: 99%

“…Zhang et al. [22] proposed a calibration‐free multi‐view crowd counting (CF‐MVCC) method, which can directly obtain scene level counts from the density map prediction of each camera. Olmschenk [23] proposed a Semi‐Supervised Dual‐Goal Generative Adversarial Networks, which allows Dual‐Goal GAN to benefit from unlabelled data during training and improves the prediction ability of the network.…”

Section: Related Workmentioning

confidence: 99%

Domain adaptive crowd counting via dynamic scale aggregation network

Huo

Wang

Qiao

et al. 2023

IET Computer Vision

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Crowd counting is an important research topic in computer vision. Its goal is to estimate the people's number in an image. Researchers have dramatically improved counting accuracy in recent years by regressing density maps. However, because of the inherent domain shift, the model trained on an expensive manually labelled dataset (source domain) does not perform well on a dataset with scarce labels (target domain). For this issue, a novel dynamic scale aggregation network (DSANet) is proposed to reduce the gaps in style and cross‐domain head scale variations. Specifically, a practical style transfer layer is introduced to reduce the appearance discrepancy between the source and target domains. Then, the translated source and target domain samples are encoded by a generator consisting of the VGG16 network and the dynamic scale aggregation modules (DSA Modules) and produce corresponding density maps. The DSA module can adaptively adjust parameters according to the input features and effectively fuse multi‐scale information to overcome the cross‐domain head scale variations. Next, a discriminator judges the input density map from the source or target domain. Last, domain distributions are aligned through adversarial between the generator and the discriminator. The experiments show that our network outperforms the current state‐of‐the‐art methods and can improve the target domain's performance while maintaining the source domain's performance without significant degradation.

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