Lightweight Res-Connection Multi-Branch Network for Highly Accurate Crowd Counting and Localization

Crowd counting is a promising hotspot of computer vision involving crowd intelligence analysis,achieving tremendous success recently with the development of deep learning.However,there have been stillmany challenges including crowd multi-scale variations and high network complexity,etc.To tackle these issues,a lightweight Resconnection multi-branch network(LRMBNet)for highly accurate crowd counting and localization is proposed.Specifically,using improved ShuffleNet V2 as the backbone,a lightweight shallow extractor has been designed by employing the channel compression mechanism to reduce enormously the number of network parameters.A light multi-branch structure with different expansion rate convolutions is demonstrated to extract multi-scale features and enlarged receptive fields,where the information transmission and fusion of diverse scale features is enhanced via residual concatenation.In addition,a compound loss function is introduced for training themethod to improve global context information correlation.The proposed method is evaluated on the SHHA,SHHB,UCF-QNRF and UCF_CC_50 public datasets.The accuracy is better than those of many advanced approaches,while the number of parameters is smaller.The experimental results show that the proposed method achieves a good tradeoff between the complexity and accuracy of crowd counting,indicating a lightweight and high-precision method for crowd counting.

A Deep Learning-Based Crowd Counting Method and System Implementation on Neural Processing Unit Platform

In this paper, a deep learning-based method is proposed for crowdcountingproblems. Specifically, by utilizing the convolution kernel densitymap, the ground truth is generated dynamically to enhance the featureextractingability of the generator model. Meanwhile, the “cross stage partial”module is integrated into congested scene recognition network (CSRNet) toobtain a lightweight network model. In addition, to compensate for the accuracydrop owing to the lightweight model, we take advantage of “structuredknowledge transfer” to train the model in an end-to-end manner. It aimsto accelerate the fitting speed and enhance the learning ability of the studentmodel. The crowd-counting system solution for edge computing is alsoproposed and implemented on an embedded device equipped with a neuralprocessing unit. Simulations demonstrate the performance improvement ofthe proposed solution in terms of model size, processing speed and accuracy.The performance on the Venice dataset shows that the mean absolute error(MAE) and the root mean squared error (RMSE) of our model drop by32.63% and 39.18% compared with CSRNet. Meanwhile, the performance onthe ShanghaiTech PartB dataset reveals that the MAE and the RMSE of ourmodel are close to those of CSRNet. Therefore, we provide a novel embeddedplatform system scheme for public safety pre-warning applications.

Robust Counting in Overcrowded Scenes Using Batch-Free Normalized Deep ConvNet

The analysis of overcrowded areas is essential for flow monitoring,assembly control,and security.Crowd counting’s primary goal is to calculate the population in a given region,which requires real-time analysis of congested scenes for prompt reactionary actions.The crowd is always unexpected,and the benchmarked available datasets have a lot of variation,which limits the trained models’performance on unseen test data.In this paper,we proposed an end-to-end deep neural network that takes an input image and generates a density map of a crowd scene.The proposed model consists of encoder and decoder networks comprising batch-free normalization layers known as evolving normalization(EvoNorm).This allows our network to be generalized for unseen data because EvoNorm is not using statistics from the training samples.The decoder network uses dilated 2D convolutional layers to provide large receptive fields and fewer parameters,which enables real-time processing and solves the density drift problem due to its large receptive field.Five benchmark datasets are used in this study to assess the proposed model,resulting in the conclusion that it outperforms conventional models.

A Deep-CNN Crowd Counting Model for Enforcing Social Distancing during COVID19 Pandemic: Application to Saudi Arabia’s Public Places

With the emergence of the COVID19 virus in late 2019 and the declaration that the virus is a worldwide pandemic,health organizations and governments have begun to implement severe health precautions to reduce the spread of the virus and preserve human lives.The enforcement of social distancing at work environments and public areas is one of these obligatory precautions.Crowd management is one of the effective measures for social distancing.By reducing the social contacts of individuals,the spread of the disease will be immensely reduced.In this paper,a model for crowd counting in public places of high and low densities is proposed.The model works under various scene conditions and with no prior knowledge.A Deep CNN model(DCNN)is built based on convolutional neural network(CNN)structure with small kernel size and two fronts.To increase the efficiency of the model,a convolutional neural network(CNN)as the front-end and a multi-column layer with Dilated Convolution as the back-end were chosen.Also,the proposed method accepts images of arbitrary sizes/scales as inputs from different cameras.To evaluate the proposed model,a dataset was created from images of Saudi people with traditional and non-traditional Saudi outfits.The model was also trained and tested on some existing datasets.Compared to current counting methods,the results show that the proposed model has significantly improved efficiency and reduced the error rate.We achieve the lowest MAE by 67%,32%.and 15.63%and lowest MSE by around 47%,15%and 8.1%than M-CNN,Cascaded-MTL,and CSRNet respectively.

Multi-Scale Network with Integrated Attention Unit for Crowd Counting

Estimating the crowd count and density of highly dense scenes witnessed in Muslim gatherings at religious sites in Makkah and Madinah is critical for developing control strategies and organizing such a large gathering.Moreover,since the crowd images in this case can range from low density to high density,detection-based approaches are hard to apply for crowd counting.Recently,deep learning-based regression has become the prominent approach for crowd counting problems,where a density-map is estimated,and its integral is further computed to acquire the final count result.In this paper,we put forward a novel multi-scale network(named 2U-Net)for crowd counting in sparse and dense scenarios.The proposed framework,which employs the U-Net architecture,is straightforward to implement,computationally efficient,and has single-step training.Unpooling layers are used to retrieve the pooling layers’erased information and learn hierarchically pixelwise spatial representation.This helps in obtaining feature values,retaining spatial locations,and maximizing data integrity to avoid data loss.In addition,a modified attention unit is introduced and integrated into the proposed 2UNet model to focus on specific crowd areas.The proposed model concentrates on balancing the number of model parameters,model size,computational cost,and counting accuracy compared with other works,which may involve acquiring one criterion at the expense of other constraints.Experiments on five challenging datasets for density estimation and crowd counting have shown that the proposed model is very effective and outperforms comparable mainstream models.Moreover,it counts very well in both sparse and congested crowd scenes.The 2U-Net model has the lowest MAE in both parts(Part A and Part B)of the ShanghaiTech,UCSD,and Mall benchmarks,with 63.3,7.4,1.5,and 1.6,respectively.Furthermore,it obtains the lowest MSE in the ShanghaiTech-Part B,UCSD,and Mall benchmarks with 12.0,1.9,and 2.1,respectively.

Adaptive Scheme for Crowd Counting Using off-the-Shelf Wireless Routers

Since the outbreak of the world-wide novel coronavirus pandemic,crowd counting in public areas,such as in shopping centers and in commercial streets,has gained popularity among public health administrations for preventing the crowds from gathering.In this paper,we propose a novel adaptive method for crowd counting based on Wi-Fi channel state information(CSI)by using common commercial wireless routers.Compared with previous researches on device-free crowd counting,our proposed method is more adaptive to the change of environ-ment and can achieve high accuracy of crowd count estimation.Because the dis-tance between access point(AP)and monitor point(MP)is typically non-fixed in real-world applications,the strength of received signals varies and makes the tra-ditional amplitude-related models to perform poorly in different environments.In order to achieve adaptivity of the crowd count estimation model,we used convo-lutional neural network(ConvNet)to extract features from correlation coefficient matrix of subcarriers which are insensitive to the change of received signal strength.We conducted experiments in university classroom settings and our model achieved an overall accuracy of 97.79%in estimating a variable number of participants.

A Deep Learning Approach for Crowd Counting in Highly Congested Scene

With the rapid progress of deep convolutional neural networks,several applications of crowd counting have been proposed and explored in the literature.In congested scene monitoring,a variety of crowd density estimating approaches has been developed.The understanding of highly congested scenes for crowd counting during Muslim gatherings of Hajj and Umrah is a challenging task,as a large number of individuals stand nearby and,it is hard for detection techniques to recognize them,as the crowd can vary from low density to high density.To deal with such highly congested scenes,we have proposed the Congested Scene Crowd Counting Network(CSCC-Net)using VGG-16 as a core network with its first ten layers due to its strong and robust transfer learning rate.A hole dilated convolutional neural network is used at the back end to widen the relevant field to extract a large range of information from the image without losing its original resolution.The dilated convolution neural network is mainly chosen to expand the kernel size without changing other parameters.Moreover,several loss functions have been applied to strengthen the evaluation accuracy of the model.Finally,the entire experiments have been evaluated using prominent data sets namely,ShanghaiTech parts A,B,UCF_CC_50,and UCF_QNRF.Our model has achieved remarkable results i.e.,68.0 and 9.0 MAE on ShanghaiTech parts A,B,199.1 MAE on UCF_CC_50,and 99.8 on UCF_QNRF data sets respectively.

Crowd Counting Based on WiFi Channel State Information and Transfer Learning

With the popularity and development of indoor WiFi equipment, they have more sensing capability and can be used as a human monitoring device. We can collect the channel state information (CSI) from WiFi device and acquire the human state based on the measurements. These studies have attracted wide attention and become a hot research topic. This paper concentrated on the crowd counting based on CSI and transfer learning. We utilized the CSI signal fluctuations caused by human motion in WiFi coverage to identify the person count because different person counts would lead to unique signal propagation characteristics. First, this paper presented recent studies of crowd counting based on CSI. Then, we introduced the basic concept of CSI, and described the fundamental principle of CSI-based crowd counting. We also presented the system framework, experiment scenario, and neural network structure transferred from the ResNet. Next, we presented the experiment results and compared the accuracy using different neural network models. The system achieved recognition accuracy of this 100 percent for seven participants using the transfer learning technique. Finally, we concluded the paper by discussing the current problems and future work.

Crowd Counting for Real Monitoring Scene

Crowd counting is a challenging task in computer vision as realistic scenes are al?ways filled with unfavourable factors such as severe occlusions, perspective distortions and di?verse distributions. Recent state-of-the-art methods based on convolutional neural network (CNN) weaken these factors via multi-scale feature fusion or optimal feature selection through a front switch-net. L2 regression is used to regress the density map of the crowd, which is known to lead to an average and blurry result, and affects the accuracy of crowd count and po?sition distribution. To tackle these problems, we take full advantage of the application of gen?erative adversarial networks (GANs) in image generation and propose a novel crowd counting model based on conditional GANs to predict high-quality density maps from crowd images. Furthermore, we innovatively put forward a new regularizer so as to help boost the accuracy of processing extremely crowded scenes. Extensive experiments on four major crowd counting datasets are conducted to demonstrate the better performance of the proposed approach com?pared with recent state-of-the-art methods.

Deep Learning Based Efficient Crowd Counting System

Estimation of crowd count is becoming crucial nowadays,as it can help in security surveillance,crowd monitoring,and management for different events.It is challenging to determine the approximate crowd size from an image of the crowd’s density.Therefore in this research study,we proposed a multi-headed convolutional neural network architecture-based model for crowd counting,where we divided our proposed model into two main components:(i)the convolutional neural network,which extracts the feature across the whole image that is given to it as an input,and(ii)the multi-headed layers,which make it easier to evaluate density maps to estimate the number of people in the input image and determine their number in the crowd.We employed the available public benchmark crowd-counting datasets UCF CC 50 and ShanghaiTech parts A and B for model training and testing to validate the model’s performance.To analyze the results,we used two metrics Mean Absolute Error(MAE)and Mean Square Error(MSE),and compared the results of the proposed systems with the state-of-art models of crowd counting.The results show the superiority of the proposed system.

比例融合与多层规模感知的人群计数方法

针对密集场景下人群图像拍摄视角或距离多变造成的多尺度特征获取不足、融合不佳和全局特征利用不充分等问题,提出一种比例融合与多层规模感知的人群计数网络。首先采用骨干网络VGG16提取人群密度初始特征;其次,设计多层规模感知模块,获得人群多尺度信息的丰富表达;再次,提出比例融合策略,根据卷积层捕获的特征权重重构多尺度信息,提取显著性人群特征;最后,采用卷积回归策略进行密度图的回归。同时,提出一种局部一致性损失函数,通过区域化密度图的方式增强生成密度图与真实密度图的相似度,提高计数性能。在多个人群数据集上的试验结果表明,所提模型优于近年人群计数的先进方法,且在车辆计数上有较好推广性。

基于多尺度金字塔Transformer的人群计数方法

针对密集人群场景中背景复杂、目标尺度变化较大导致人群计数精度较低的问题,本文提出一种基于多尺度金字塔Transformer的人群计数方法(multi-scale pyramid transformer network,MSPT-Net)。在特征提取阶段设计了一种基于深度可分离自注意力的金字塔Transformer主干网络结构,该网络结构能有效捕获图像的局部和全局信息,从而有效解决人群密度图像背景复杂导致计数精度低的问题;设计了一种特征金字塔融合模块及多尺度感受野的回归头,实现了密集人群图像浅层细节特征和深层语义特征的高效融合,增强了网络对不同尺度目标的捕获能力;采用深度监督的训练方法在3个公开数据集上对提出的方法进行验证。实验结果表明,本文方法在全监督与弱监督学习策略中,与目前主流的人群计数方法相比,实现了更高精度的人群计数,克服了主流方法对背景复杂、目标尺度变化大的密集人群图像计数精度低的问题,同时本文方法保持着更小的参数量与计算量。

基于多分支特征融合的密集人群计数网络

针对人群计数任务中存在的多尺度变化、背景噪声等问题,提出一种基于多分支特征融合的人群计数网络。在网络前端设计一个双向特征融合路径,将网络深层的语义信息和浅层的空间细节信息进行反复提取融合,使用位置注意力机制和通道注意力机制增强网络对人群和背景之间的判别能力,生成高质量特征图;网络后端采用密集残差连接增强网络对人头连续的多尺度信息提取能力,得到最终的人群密度图。在ShanghaiTech、UCF_CC_50和UCF_QNRF数据集上分别进行的对比实验的结果表明,该模型的计数性能优于先前诸多方法,有着良好的计数精度。

基于注意力机制的多尺度融合人群计数算法

针对人群计数图像人头尺度变化大、背景噪声高等问题,提出一种基于注意力机制的多尺度融合人群计数算法,以充分聚合多尺度信息,并有效区分背景噪声。构建基于残差连接的空洞空间金字塔池化,通过残差结构以及多个不同扩张率的空洞卷积在捕获多尺度头部目标特征的同时融入浅层特征图的空间细节信息,提高特征图质量;构建跨层多尺度特征融合模块,融合浅层和深层分支不同大小的边缘细节信息和上下文语义信息,并设计基于多分支的特征融合模块,融合不同感受野大小的多尺度信息以缓解大规模人头尺度变化的问题;构建基于矩阵相似运算的通道和空间注意力机制模块提取像素级特征权重,加强网络对于背景和人头目标的判别能力,自适应矫正位置信息。实验结果表明,相比11种对比算法的最优值,所提算法在SHA数据集上的平均绝对误差和均方根误差指标降低1.4%、4.2%,在UCF_CC_50数据集上降低4.9%、1.8%,能够精确地预测人群分布状态和估计人群数量,生成高质量的人群密度图。

结合单列多列神经网络的移动状态人群计数方法研究

已有人群计数方法局限于对人群的全部进行计数,在仅对人群中的移动者进行计数时准确率较低,基于注意力的多阶段深度学习框架被提出以解决这一问题。通过注意力机制适应性地在单列和多列计数网络进行选择,结合单列网络的深层特征表示能力和多列网络多尺度特征学习能力,有效提取人群中移动者的特征。实验结果表明,所提出的方法均方误差(MSE)和平均绝对误差(MAE)皆低于已有人群计数方法,能够有效提高处于移动状态的人群的计数精度。

基于深度学习的到课率统计系统设计与实现

到课率作为宏观教学管理数据,对高校教学管理具有重要作用。虽然近年来出现了一些课率统计的数字化方法,解决了传统到课率统计费时、费力、滞后等问题,但由于成本高、使用不方便、准确率不高等原因,导致其无法推广。随着技术的发展,深度学习在多目标检测中的准确率越来越高,有助于解决此类问题。为此,文章利用深度学习技术,设计了一种基于教室摄像头RTSP视频流的到课学生头部识别的模型1MB-Plus,并将其应用于某高校的一百余间教室的到课率统计中,取得了97.3%的准确率。研究表明,该模型有助于解决到课率统计存在的问题。文章通过研究,旨在以最小的成本为高校教务管理部门提供较为准确的宏观到课率数据,辅助学校的教学管理工作。

基于深度学习的口罩佩戴检测与人群聚集预警系统

为了提高新冠疫情防控的效率和范围,提出一种基于深度学习的口罩佩戴检测与人群聚集预警系统,该系统包含口罩佩戴检测、人群聚集检测以及智能场景分类模块。智能场景分类模块使用深度学习分类算法自动识别摄像头机位种类,从而对低机位的摄像头中的画面进行准确且实时的口罩佩戴检测,人群聚集检测模块能够快速计算出高机位的广角摄像头画面中的人数,判断是否存在大规模人群聚集,从而有效提高疫情防控的效率与范围。实验证明,系统在各种不同的摄像头画面下均能准确判断摄像头机位并进行准确快速的口罩佩戴检测以及人群聚集检测。

Scene-adaptive crowd counting method based on meta learning with dual-input network DMNet

Crowd counting is recently becoming a hot research topic, which aims to count the number of the people in different crowded scenes. Existing methods are mainly based on training-testing pattern and rely on large data training, which fails to accurately count the crowd in real-world scenes because of the limitation of model’s generalization capability. To alleviate this issue, a scene-adaptive crowd counting method based on meta-learning with Dual-illumination Merging Network (DMNet) is proposed in this paper. The proposed method based on learning-to-learn and few-shot learning is able to adapt different scenes which only contain a few labeled images. To generate high quality density map and count the crowd in low-lighting scene, the DMNet is proposed, which contains Multi-scale Feature Extraction module and Element-wise Fusion Module. The Multi-scale Feature Extraction module is used to extract the image feature by multi-scale convolutions, which helps to improve network accuracy. The Element-wise Fusion module fuses the low-lighting feature and illumination-enhanced feature, which supplements the missing illumination in low-lighting environments. Experimental results on benchmarks, WorldExpo’10, DISCO, USCD, and Mall, show that the proposed method outperforms the existing state-of-the-art methods in accuracy and gets satisfied results.

DTCC:Multi-level dilated convolution with transformer for weakly-supervised crowd counting

Crowd counting provides an important foundation for public security and urban management.Due to the existence of small targets and large density variations in crowd images,crowd counting is a challenging task.Mainstream methods usually apply convolution neural networks(CNNs)to regress a density map,which requires annotations of individual persons and counts.Weakly-supervised methods can avoid detailed labeling and only require counts as annotations of images,but existing methods fail to achieve satisfactory performance because a global perspective field and multi-level information are usually ignored.We propose a weakly-supervised method,DTCC,which effectively combines multi-level dilated convolution and transformer methods to realize end-to-end crowd counting.Its main components include a recursive swin transformer and a multi-level dilated convolution regression head.The recursive swin transformer combines a pyramid visual transformer with a fine-tuned recursive pyramid structure to capture deep multi-level crowd features,including global features.The multi-level dilated convolution regression head includes multi-level dilated convolution and a linear regression head for the feature extraction module.This module can capture both low-and high-level features simultaneously to enhance the receptive field.In addition,two regression head fusion mechanisms realize dynamic and mean fusion counting.Experiments on four well-known benchmark crowd counting datasets(UCF_CC_50,ShanghaiTech,UCF_QNRF,and JHU-Crowd++)show that DTCC achieves results superior to other weakly-supervised methods and comparable to fully-supervised methods.

基于可变形高斯核的训练数据生成的人群计数方法

人群计数作为计算机视觉和模式识别任务中重要的子课题,在智能监控中发挥着极其重要的作用。对于被严重遮挡的月牙形人头,传统高斯核生成方法找到的月牙形视觉中心严重偏离人类标注的完整圆形中心,导致算法在训练中不易收敛。针对严重遮挡情况下的人群计数误差问题,提出一种基于可变形高斯核的训练数据生成的人群计数方法,对基于人类标定结果生成的高斯核的形状、角度和位置进行高效调整,从而提升算法的收敛性和精度。实验结果表明,该方法可以显著提升人群计数的性能。