Intelligent Real-Time Crowd Density Estimation for Proactive Event Safety: A Machine Learning Approach

Accurately estimate the crowd count from a still image with arbitrary perspective and arbitrary crowd density is one of the difficulties of crowd analysis in surveillance videos. Conventional methods are scene-specific and subject to occlusions. In this paper, we propose a Multi-task Multi-column Convolutional Neural Network (MMCNN) architecture for crowd counting and crowd density estimation in still images of surveillance scenes. The MMCNN architecture is an end-to-end system which is robust for images with different perspective and different crowd density. By promoting MCNN with \(3\times 3\) filter, the MMCNN could utilize local spatial features from each column. Furthermore, the ground truth density map is generated based on Perspective-Adaptive Gaussian kernels which can better represent the heads of pedestrians. Finally, we use an iterative switching process in our deep crowd model to alternatively optimize the crowd density map estimation task and crowd counting task. We conduct experiments on the WorldExpo’10 dataset and our method achieves better results.

The purpose of crowd counting is to estimate the number of pedestrians in crowd images. Crowd counting or density estimation is an extremely challenging task in computer vision, due to large scale variations and dense scene. Current methods solve these issues by compounding multi-scale Convolutional Neural Network with different receptive fields. In this paper, a novel end-to-end architecture based on Multi-Scale Adversarial Convolutional Neural Network (MSA-CNN) is proposed to generate crowd density and estimate the amount of crowd. Firstly, a multi-scale network is used to extract the globally relevant features in the crowd image, and then fractionally-strided convolutional layers are designed for up-sampling the output to recover the loss of crucial details caused by the earlier max pooling layers. An adversarial loss is directly employed to shrink the estimated value into the realistic subspace to reduce the blurring effect of density estimation. Joint training is performed in an end-to-end fashion using a combination of Adversarial loss and Euclidean loss. The two losses are integrated via a joint training scheme to improve density estimation performance.We conduct some extensive experiments on available datasets to show the significant improvements and supremacy of the proposed approach over the available state-of-the-art approaches.

This paper focuses on crowd motion analysis and consists two parts. Firstly, we propose a new foreground detection approach called optical flow and background model (OFBM) based on Lucas-Kanade optical flow and Gaussian background model methods. This approach overcomes the shortages of optical flow and background subtract, such as sensitiveness of light changing and producing accumulate errors. Secondly, according to moment analysis, we propose a new feature based on the zeroth-order Tehebichef discrete orthogonal moment (TOM), which is employed for crowd density estimation. Some experimental results show that this approach is useful and efficient in crowd density estimation.

The estimation of crowd density is a crucial area of study in computer vision because of its extensive use in intelligence collection, urban planning, and surveillance. The crowd density prediction that comes from a thorough analysis considers several factors, including inter-blocking in dense crowds, background elements, and individual appearance similarity. To monitor populous areas and avoid congestion, we are interested in using machine learning for crowd control in this research. We suggest using a Single Convolutional Neural Network with Three Layers (S-CNN3) model to estimate the crowd size and count the number of individuals in a scene. Next, a comparison study for density counting determines how well the suggested model performs in comparison to switched convolutional neural networks (SCNN) and convolutional neural networks with four layers (single CNN4). This study makes use of the Shanghai Tech dataset, which is regarded as the biggest database for crowd counting. With an average test accuracy of 99.88% and an average validation loss of 0.02 for crowd density estimate, the suggested model demonstrates remarkable efficacy and efficiency. These outcomes outperform the state-of-the-art models already in use.

Video-based crowd counting and density estimation (CCDE) is vital for crowd monitoring. The existing solutions lack in addressing issues like cluttered background and scale variation in crowd videos. To this end, a multiscale head attention-guided multiscale density maps fusion for video-based CCDE via multi-attention Spatial-Temporal CNN (MHAMD-MST-CNN) is proposed. The MHAMD-MST-CNN has three modules: a multi attention spatial stream (MASS), a multi attention temporal stream (MATS), and a final density map generation (FDMG) module. The spatial head attention modules (SHAMs) and temporal head attention modules (THAMs) are designed to eliminate the background influence from the MASS and the MATS, respectively, by mapping the multiscale spatial or temporal features to head maps. The multiscale de-backgrounded features are utilised by the density map generation (DMG) modules to generate multiscale density maps to deal with scale variation due to perspective distortion. The multiscale density maps are fused and fed into the FDMG module to obtain the final crowd density map. The MHAMD-MST-CNN has been trained and validated on three publicly available benchmark datasets: the Venice, the Mall, and the UCSD. The MHAMD-MST-CNN provides competitive results as compared with the state-of-the-arts in terms of mean absolute error (MAE) and root mean squared error (RMSE).

Crowd density estimation and crowd counting has acquired importance towards Machine learning and Deep learning industry due to the improvement in performance, when compared to traditional computer vision techniques. This paper presents deep learning based optimal dimension convolutional neural network (CNN) for estimating crowd density, which is used to classify images of crowd into various density levels such as low crowd, very low crowd, moderate crowd, high crowd, very high crowd. This approach is experimented on existing datasets and gives the better accuracy with optimum network dimensions.

Human beings perceive images through their properties, like colour, shape, size, and texture. Texture is a fertile source of information about the physical environment. Images of low density crowds tend to present coarse textures, while images of dense crowds tend to present fine textures. This paper describes a new technique for automatic estimation of crowd density, which is a part of the problem of automatic crowd monitoring, using texture information based on grey-level transition probabilities on digitised images. Crowd density feature vectors are extracted from such images and used by a self organising neural network which is responsible for the crowd density estimation. Results obtained respectively to the estimation of the number of people in a specific area of Liverpool Street Railway Station in London (UK) are presented.

We address the problem of detecting highly raised crowd density in situations such as indoor dance events. We propose a new method for estimating crowd density by anonymous, non-participatory, indoor Wi-Fi localization of smart phones. Using a probabilistic model inspired by statistical mechanics, and relying only on big data analytics, we tackle three challenges: (1) the ambiguity of Wi-Fi based indoor positioning, which appears regardless of whether the latter is performed with machine learning or with optimization, (2) the MAC address randomization when a device is not connected, and (3) the volatility of packet interarrival times. The main result is that our estimation becomes more—rather than less—accurate when the crowd size increases. This property is crucial for detecting dangerous crowd density.

The task of crowd counting and density map estimation is riddled with many challenges, such as occlusions, non-uniform density, intra-scene and inter-scene variations in scale and perspective. Due to the development of deep learning and large crowd datasets in recent years, most crowd counting methods have achieved notable success. This paper aims to solve crowd density estimation problem for both sparse and dense conditions. To this end, we make two contributions: (1) a network named Patch Scale Discriminant Regression Network (PSDR). Given an input crowd image, it divides the image into patches and sends image patches of different density levels into different regression networks to get the corresponding density maps. It combines all patch density maps to predict the entire density map as the output. (2) A person classification activation map (CAM) method. CAM provides person location information and guides the generation of the entire density map in the final stage. Experiment confirms that CAM allows PSDR to gain another round of performance boost. For instance, on the SmartCity dataset, we achieve (8.6–1.1) MAE and (11.6–1.4) MSE. Our method combining above two methods performs better than state-of-the-art methods.

In public areas, crowd stampedes and incidents generate huge negative impacts on public security. Accurate and efficient crowd density estimation is critical to monitor crowd status for developing evacuation strategies. The existing crowd density estimation methods are established based on complex deep-learning algorithms which are usually more accurate, but, on the other side, they require much more computational resources. Consequently, cloud-computing is the only option for deploying crowd density estimation algorithms, which needs tremendous resources for real-time video data transmission and the malfunction and delay of internet service may cause wrong and delayed estimation results. Edge computing is a novel concept of accomplishing computing tasks only relying on the computational resources of edge devices. Estimating crowd density on edge devices rather than conveying images to cloud server for further analysis has several advantages, including 1) reducing network bandwidth pressure from remote transmission; 2) avoiding risks of leaking privacy on images; and 3) improving computation efficient. This study designs an edge computing-enabled crowd density estimation model based on the residual bottleneck block and dilated convolution. The experiments are designed and conducted on public crowd data sets to verify accuracy, storage cost and computation efficiency of our model. According to the experimental results, the proposed model achieves a considerable improvement in operational efficiency, while keep the accuracy at the same level with the complex deep-learning algorithms. Furthermore, the proposed model is implemented on a real edge device to detect real-world crowd density in a Beijing subway station.

The rapid growth of metropolitan populations and large public gatherings has increased the need for intelligent surveillance systems capable of ensuring public safety. This study proposes a novel framework, Intelligent Anomaly Detection and Localization with Crowd Density Estimation (IADL-CDE), which jointly addresses crowd density classification and anomaly detection in real-time. Leveraging the ShanghaiTech dataset for crowd density estimation and the UCSD Pedestrian Database for anomaly detection, the framework integrates a Robust Maximum Correntropy Kalman Filter (RMCKF) for effective image pre-processing, followed by a Self-Modulating Convolutional Neural Network (SMCNN) that accurately classifies crowd density levels into sparse, medium, and dense categories. Subsequently, a Pixel-Associated Auto Encoder (PAAE) is employed to detect and localize anomalies such as cycling, skateboarding, and unauthorized vehicles in pedestrian environments. Experimental results demonstrate that the proposed IADL-CDE model achieves significant improvements in accuracy, precision, recall, F1-Score and Cohen’s Kappa Coefficient compared to the baseline methods. These findings highlight the model’s effectiveness and robustness in enhancing public safety through intelligent crowd monitoring and anomaly detection.

Estimating crowd density and counting people are essential for crowd control, urban planning, and public safety. This research study utilizes a Multi-Column Convolutional Neural Network (MC-CNN) as a crowd counting technique trained on crowd datasets. The MC-CNN predicts crowd density maps from input images, providing precise crowd density estimates. Additionally, post-processing methods like clustering analyze the spatial crowd distribution. The proposed system incorporates object detection algorithms to generate individual coordinates, aiding in crowd analysis through clustering and then classifying them into three categories i.e. sparse, medium, and dense. Experimental findings showcase the method’s efficacy in accurately estimating crowd densities and discerning crowd patterns. The proposed system provides valuable insights for crowd monitoring, resource allocation, and decision-making in densely populated areas.

Due to the rapid increase in urbanization and population, crowd gatherings are frequently observed in the form of concerts, political, and religious meetings. HAJJ is one of the well-known crowding events that takes place every year in Makkah, Saudi Arabia. Crowd density estimation and crowd monitoring are significant research areas in Artificial Intelligence (AI) applications. The current research study develops a new Sparrow Search Optimization with Deep Transfer Learning based Crowd Density Detection and Classification (SSODTL-CD2C) model. The presented SSODTL-CD2C technique majorly focuses on the identification and classification of crowd densities. To attain this, SSODTL-CD2C technique exploits Oppositional Salp Swarm Optimization Algorithm (OSSA) with EfficientNet model to derive the feature vectors. At the same time, Stacked Sparse Auto Encoder (SSAE) model is utilized for the classification of crowd densities. Finally, SSO algorithm is employed for optimal fine-tuning of the parameters involved in SSAE mechanism. The performance of the proposed SSODTL-CD2C technique was validated using a dataset with four different kinds of crowd densities. The obtained results demonstrated that the proposed SSODTL-CD2C methodology accomplished an excellent crowd classification performance with a maximum accuracy of 93.25%. So, the proposed method will be highly helpful in managing HAJJ and other crowded events.

This paper introduces an approach to select the bandwidth or smoothing parameter in multiresolution (MR) density estimation and nonparametric density estimation. It is based on the evolution of the second, third and fourth central moments and the shape of the estimated densities for different bandwidths and resolution levels. The proposed method has been applied to density estimation by means of multiresolution densities as well as kernel density estimation (MRDE and KDE respectively). The results of the simulations and the empirical application demonstrate that the level of resolution resulting from the moments method performs better with multimodal densities than the Bayesian Information Criterion (BIC) for multiresolution densities estimation and the plug-in for kernel densities estimation.

In this paper, we focus on the task of estimating crowd count and high-quality crowd density maps. Among crowd counting methods, crowd density map estimation is especially promising because it preserves spatial information which makes it useful for both counting and localization (detection and tracking). Convolutional neural networks have enabled significant progress in crowd density estimation recently, but there are still open questions regarding suitable architectures. We revisit CNNs design and point out key adaptations, enabling plain a signal column CNNs to obtain high resolution and high-quality density maps on all major dense crowd counting datasets. The regular deep supervision utilizes the general ground truth to guide intermediate predictions. Instead, we build hierarchical supervisory signals with additional multi-scale labels to consider the diversities in deep neural networks. We begin by obtaining multi-scale labels based on different Gaussian kernels. These multi-scale labels can be seen as diverse representations in the supervision and can achieve high performance for better quality crowd density map estimation. Extensive experiments demonstrate that our approach achieves the state-of-the-art performance on the ShanghaiTech, UCF_CC_50 and UCSD datasets.