NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization

Wang, Qi; Ji, Gao; Lin, Wei; Li, Xuelong

doi:10.1109/tpami.2020.3013269

Cited by 279 publications

(159 citation statements)

References 36 publications

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“…[22][23][24][25][26][27][28] proposed data-driven and adaptive methods that can understand highly congested scenes and perform well. Other works [29][30][31][32][33] used the multi-scale networks for image crowd counting, which are both accurate and cost-effective for practical applications. Cheng et al [34] proposed a novel architecture called spatial awareness network to incorporate spatial context for crowd counting.…”

Section: Map-based Methods For Crowd Countingmentioning

confidence: 99%

MSR‐FAN: Multi‐scale residual feature‐aware network for crowd counting

Zhao

Min

Wei

et al. 2021

IET Image Processing

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Crowd counting aims to count the number of people in crowded scenes, which is important to the security systems, traffic control and so on. The existing methods typically using local features cannot properly handle the perspective distortion and the varying scales in congested scene images, and henceforth perform wrong people counting. To alleviate this issue, this study proposes a multi-scale residual feature-aware network (MSR-FAN) that combines multi-scale features using multiple receptive field sizes and learns the featureaware information on each image. The MSR-FAN is trained end-to-end to generate highquality density map and evaluate the crowd number. The method consists of three parts. To handle the perspective changes problem, the first part, the direction-based featureenhanced network, is designed to encode the perspective information in four directions based on the initial image feature. The second part, the proposed multi-scale residual block module, gets the global information to handle the represent the regional feature better. This module explores features of different scales as well as reinforce the global feature. The third part, the feature-aware block, is designed to extract the feature hidden in the different channels. Experiment results based on benchmark datasets show that the proposed approach outperforms the existing state-of-the-art methods.

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Section: Map-based Methods For Crowd Countingmentioning

confidence: 99%

MSR‐FAN: Multi‐scale residual feature‐aware network for crowd counting

Zhao

Min

Wei

et al. 2021

IET Image Processing

Self Cite

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“…To evaluate the effectiveness of the proposed MFP-Net, five state-of-the-art network models MCNN [5], CSRNet [47], SFCN [11] and SFCN+ [11] are considered as comparative approaches in our experiments. Besides, all comparative networks and the proposed MFP-Net are performed on five popular datasets ShanghaiTech [5], NWPU-Crowd [7], UCF_CC_50 [59], UCF-QRNF [60] and GCC [8]. In this section, we illustrate the evaluation metrics and experimental details.…”

Section: Methodsmentioning

confidence: 99%

MFP‐Net: Multi‐scale feature pyramid network for crowd counting

Lei

Zhang

Wang

et al. 2021

IET Image Processing

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Although deep learning has been widely used for dense crowd counting, it still faces two challenges. Firstly, the popular network models are sensitive to scale variance of human head, human occlusions, and complex background due to repeated utilization of vanilla convolution kernels. Secondly, the vanilla feature fusion often depends on summation or concatenation, which ignores the correlation of different features leading to information redundancy and low robustness to background noise. To address these issues, a multiscale feature pyramid network (MFP-Net) for dense crowd counting is proposed in this paper. The proposed MFP-Net makes two contributions. Firstly, the feature pyramid fusion module is designed that adopts rich convolutions with different depths and scales, not only to expand the receptive field, but also to improve the inference speed of models by using parallel group convolution. Secondly, a feature attention-aware module is added in the feature fusion stage. The module can achieve local and global information fusion by capturing the importance of the spatial and channel domains to improve model robustness. The proposed MFP-Net is evaluated on five publicly available datasets, and experiments show that the MFP-Net not only provides better crowd counting results than comparative models, but also requires fewer parameters.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Traditional approaches can be further classified to detection-, regression-and density estimation-based approaches. While the CNN-based approaches count the number of people in an image using the advancements driven primarily by CNN network (Wang et al 2020;Sindagi et al 2019;Ma et al 2019;Sindagi and Patel 2019b;Jiang et al 2019;Shi et al 2019;Sindagi and Patel 2019a). The CNN based methods can be further classified based on network property and training approach.…”

Section: Sendmentioning

confidence: 99%

“…In the meantime, a set of datasets are proposed and employed in crowd counting analysis (Wang et al 2020;Chan et al 2008;Chen et al 2012;Idrees et al 2013;Zhang et al 2015Zhang et al , 2016a. These datasets are diverse concerning the dense level and scene variation across images.…”

Section: Sendmentioning

confidence: 99%

Counting people in the crowd using social media images for crowd management in city events

et al. 2021

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City events are getting popular and are attracting a large number of people. This increase needs for methods and tools to provide stakeholders with crowd size information for crowd management purposes. Previous works proposed a large number of methods to count the crowd using different data in various contexts, but no methods proposed using social media images in city events and no datasets exist to evaluate the effectiveness of these methods. In this study we investigate how social media images can be used to estimate the crowd size in city events. We construct a social media dataset, compare the effectiveness of face recognition, object recognition, and cascaded methods for crowd size estimation, and investigate the impact of image characteristics on the performance of selected methods. Results show that object recognition based methods, reach the highest accuracy in estimating the crowd size using social media images in city events. We also found that face recognition and object recognition methods are more suitable to estimate the crowd size for social media images which are taken in parallel view, with selfies covering people in full face and in which the persons in the background have the same distance to the camera. However, cascaded methods are more suitable for images taken from top view with gatherings distributed in gradient. The created social media dataset is essential for selecting image characteristics and evaluating the accuracy of people counting methods in an urban event context.

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NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization

Cited by 279 publications

References 36 publications

MSR‐FAN: Multi‐scale residual feature‐aware network for crowd counting

MSR‐FAN: Multi‐scale residual feature‐aware network for crowd counting

MFP‐Net: Multi‐scale feature pyramid network for crowd counting

Counting people in the crowd using social media images for crowd management in city events

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