Fast crowd density estimation with convolutional neural networks

Fu, Min; Xu, Pei; Li, Xudong; Liu, Qihe; Ye, Mao; Zhu, Ce

doi:10.1016/j.engappai.2015.04.006

Cited by 216 publications

(82 citation statements)

References 13 publications

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“…Wang et al [18] posed crowd counting as a regression problem, and used a CNN model to map the input crowd image to its corresponding count. Instead of predicting the overall count, Fu et al [4] classified an image into five broad crowd density categories and used a cascade of two CNNs in a boosting like strategy where the second CNN was trained on the images misclassified by the first CNN. These methods also overlooked the benefits provided by the crowd density annotation maps.…”

Section: Related Workmentioning

confidence: 99%

Iterative Crowd Counting

Ranjan

Hoai

2018

Computer Vision – ECCV 2018

248

189

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In this work, we tackle the problem of crowd counting in images. We present a Convolutional Neural Network (CNN) based density estimation approach to solve this problem. Predicting a high resolution density map in one go is a challenging task. Hence, we present a two branch CNN architecture for generating high resolution density maps, where the first branch generates a low resolution density map, and the second branch incorporates the low resolution prediction and feature maps from the first branch to generate a high resolution density map. We also propose a multi-stage extension of our approach where each stage in the pipeline utilizes the predictions from all the previous stages. Empirical comparison with the previous state-of-the-art crowd counting methods shows that our method achieves the lowest mean absolute error on three challenging crowd counting benchmarks: Shanghaitech, World-Expo'10, and UCF datasets.

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Section: Related Workmentioning

confidence: 99%

Iterative Crowd Counting

Ranjan

Hoai

2018

Computer Vision – ECCV 2018

248

189

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“…Computer vision-based approaches [11][17][21] [12] perform classification based on the features learned from images or videos to detect people. In [11], neural networks are considered to estimate the density of crowds for improving the detection accuracy and speeds. Crowd detection technology using video content analytics is used in [21].…”

Section: Related Workmentioning

confidence: 99%

CrowdEstimator

Solmaz²

2018

Proceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services

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Crowd mobility has been paid attention for the Internet-of-things (IoT) applications. This paper addresses the crowd estimation problem and builds an IoT service to share the crowd estimation results across different systems. The crowd estimation problem is to approximate the crowd size in a targeted area using the observed information (e.g., Wi-Fi data). This paper exploits Wi-Fi probe request packets ("Wi-Fi probes" for short) broadcasted by mobile devices to solve this problem. However, using only Wi-Fi probes to estimate the crowd size may result in inaccurate results due to various environmental uncertainties which may lead to crowd overestimation or underestimation. Moreover, the ground-truth is unavailable because the coverage of Wi-Fi signals is time-varying and invisible. This paper introduces auxiliary sensors, stereoscopic cameras, to collect the near ground-truth at a specified calibration choke point. Two calibration algorithms are proposed to solve the crowd estimation problem. The key idea is to calibrate the Wi-Fionly crowd estimation based on the correlations between the two types of data modalities. Then, to share the calibrated results across systems required by different stakeholders, our system is integrated with the FIWARE-based IoT platform. To verify the proposed system, we have launched an indoor pilot study in the Wellington Railway Station and an outdoor pilot study in the Christchurch Re:START Mall in New Zealand. The large-scale pilot studies show that stereoscopic cameras can reach minimum accuracy of 85% and high precision detection for providing the near ground-truth. The proposed calibration algorithms reduce estimation errors by 43.68% on average compared to the Wi-Fi-only approach. CCS CONCEPTS• Computer systems organization → Embedded and cyberphysical systems;©ACM 2018. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM MobiSys'18, http://doi.

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“…We leverage imagelevel labels, which are much easier to obtain as compared to point-wise annotations 1 , in a weakly supervised fashion for fine-tuning networks to newer datasets/scenes. To achieve this weak supervision, we use the idea of image-level labeling of crowd images into different density levels by Sindagi et al [4] and Fu et al [20]. While these methods [4,20] employ image-level labels in conjunction to point-wise annotations to train their networks, we propose to use only image-level labels in the weakly supervised setup while adapting to new datasets, thereby avoiding the labour intensive point-wise annotation process.…”

Section: Introductionmentioning

confidence: 99%

HA-CCN: Hierarchical Attention-Based Crowd Counting Network

Sindagi

Patel

2020

IEEE Trans. on Image Process.

173

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Single image-based crowd counting has recently witnessed increased focus, but many leading methods are far from optimal, especially in highly congested scenes. In this paper, we present Hierarchical Attention-based Crowd Counting Network (HA-CCN) that employs attention mechanisms at various levels to selectively enhance the features of the network. The proposed method, which is based on the VGG16 network, consists of a spatial attention module (SAM) and a set of global attention modules (GAM). SAM enhances low-level features in the network by infusing spatial segmentation information, whereas the GAM focuses on enhancing channel-wise information in the higher level layers. The proposed method is a single-step training framework, simple to implement and achieves state-of-the-art results on different datasets. Furthermore, we extend the proposed counting network by introducing a novel set-up to adapt the network to different scenes and datasets via weak supervision using image-level labels. This new set up reduces the burden of acquiring labour intensive point-wise annotations for new datasets while improving the cross-dataset performance.

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Fast crowd density estimation with convolutional neural networks

Cited by 216 publications

References 13 publications

Iterative Crowd Counting

Iterative Crowd Counting

CrowdEstimator

HA-CCN: Hierarchical Attention-Based Crowd Counting Network

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