MTCNET: Multi-task Learning Paradigm for Crowd Count Estimation

Our work proposes a novel deep learning framework for estimating crowd density from static images of highly dense crowds. We use a combination of deep and shallow, fully convolutional networks to predict the density map for a given crowd image. Such a combination is used for effectively capturing both the high-level semantic information (face/body detectors) and the low-level features (blob detectors), that are necessary for crowd counting under large scale variations. As most crowd datasets have limited training samples (<100 images) and deep learning based approaches require large amounts of training data, we perform multiscale data augmentation. Augmenting the training samples in such a manner helps in guiding the CNN to learn scale invariant representations. Our method is tested on the challenging UCF CC 50 dataset, and shown to outperform the state of the art methods.

Computers, Materials &amp; Continua

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.

Big Data and Cognitive Computing, 2021

Automatically estimating the number of people in unconstrained scenes is a crucial yet challenging task in different real-world applications, including video surveillance, public safety, urban planning, and traffic monitoring. In addition, methods developed to estimate the number of people can be adapted and applied to related tasks in various fields, such as plant counting, vehicle counting, and cell microscopy. Many challenges and problems face crowd counting, including cluttered scenes, extreme occlusions, scale variation, and changes in camera perspective. Therefore, in the past few years, tremendous research efforts have been devoted to crowd counting, and numerous excellent techniques have been proposed. The significant progress in crowd counting methods in recent years is mostly attributed to advances in deep convolution neural networks (CNNs) as well as to public crowd counting datasets. In this work, we review the papers that have been published in the last decade and provi...

Computer Science and Information Systems, 2022

Crowd counting has a range of applications and it is an important task that can help with the accident prevention such as crowd crushes and stampedes in political protests, concerts, sports, and other social events. Many crown counting approaches have been proposed in the recent years. In this paper we compare five deep-learning-based approaches to crowd counting, reevaluate them and present a novel CSRNet-based approach. We base our implementation on five convolutional neural network (CNN) architectures: CSRNet, Bayesian Crowd Counting, DM-Count, SFA-Net, and SGA-Net and present a novel approach by upgrading CSRNet with application of a Bayesian crowd counting loss function and pixel modeling. The models are trained and evaluated on three widely used crowd image datasets, ShanghaiTech part A, part B, and UCF-QNRF. The results show that models based on SFA-Net and DM-Count outperform state-of-the-art when trained and evaluated on the similar data, and the proposed extended model outperforms the base model with the same backbone when trained and evaluated on the significantly different data, suggesting improved robustness levels.

2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019

In this work, we propose a novel crowd counting network that progressively generates crowd density maps via residual error estimation. The proposed method uses VGG16 as the backbone network and employs density map generated by the final layer as a coarse prediction to refine and generate finer density maps in a progressive fashion using residual learning. Additionally, the residual learning is guided by an uncertainty-based confidence weighting mechanism that permits the flow of only high-confidence residuals in the refinement path. The proposed Confidence Guided Deep Residual Counting Network (CG-DRCN) is evaluated on recent complex datasets, and it achieves significant improvements in errors. Furthermore, we introduce a new large scale unconstrained crowd counting dataset (JHU-CROWD) that is ∼2.8 × larger than the most recent crowd counting datasets in terms of the number of images. It contains 4,250 images with 1.11 million annotations. In comparison to existing datasets, the proposed dataset is collected under a variety of diverse scenarios and environmental conditions. Specifically, the dataset includes several images with weatherbased degradations and illumination variations in addition to many distractor images, making it a very challenging dataset. Additionally, the dataset consists of rich annotations at both image-level and head-level. Several recent methods are evaluated and compared on this dataset.

Journal of Intelligent Systems, 2020

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 a...

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017

We propose a novel crowd counting model that maps a given crowd scene to its density. Crowd analysis is compounded by myriad of factors like inter-occlusion between people due to extreme crowding, high similarity of appearance between people and background elements, and large variability of camera viewpoints. Current state-of-the art approaches tackle these factors by using multi-scale CNN architectures, recurrent networks and late fusion of features from multi-column CNN with different receptive fields. We propose switching convolutional neural network that leverages variation of crowd density within an image to improve the accuracy and localization of the predicted crowd count. Patches from a grid within a crowd scene are relayed to independent CNN regressors based on crowd count prediction quality of the CNN established during training. The independent CNN regressors are designed to have different receptive fields and a switch classifier is trained to relay the crowd scene patch to the best CNN regressor. We perform extensive experiments on all major crowd counting datasets and evidence better performance compared to current stateof-the-art methods. We provide interpretable representations of the multichotomy of space of crowd scene patches inferred from the switch. It is observed that the switch relays an image patch to a particular CNN column based on density of crowd.

IEEExplore, 2019 Innovations in Intelligent Systems and Applications Conference, 2019

In this study, a novel and efficient deep learning model are proposed to estimate the number of people in highly dense crowd images. We present a convolutional neural network model consisting of two parallel modules which focus on various specific features of the images. Thus, while the general density map is derived by obtaining lower-level features from the first module, it is possible to identify regions of the human body, such as head and upper body with the help of the higher-level features in the deeper second module. These two modules are then concatenated with a fully connected neural network. The proposed model was tested with the ShanghaiTech Part-A dataset. The mean square error and mean absolute error values are used as performance metrics. By comparing these metrics regarding recent studies, more successful results were obtained by using the proposed method.

IEEE Access

Nowadays, crowd analysis is one of the most important concepts that needs be relied upon, it contributes to decision making and ensuring the safety and security of the crowd. There are a variety of interesting research problems within the scope of crowd analysis including crowd tracking, crowd behaviour recognition and crowd counting. Crowd counting based on images and videos has been studied in past years. Nonetheless, estimating and detecting the number of human heads remains a challenging task due to occlusions, resolution, and lighting changes. This paper provides an overview and performance comparison of crowd counting techniques using convolutional neural networks (CNN) based on density map estimation. In this paper, we present a comprehensive analysis and benchmarking of crowd counting based on the UCF-QNRF dataset that contains the largest number of crowd count images and head annotations available in the public domain. We also show the density maps generation and their empirical evaluation along with performance comparison. INDEX TERMS Large-scale crowd, crowd counting, computer vision, deep learning, convolutional neural networks, bio-inspired model, density map estimation.