Crowd density estimation via Markov Random Field (MRF)

Guo, Jinnian; Wang, Xinyu; Cao, Tian; Yu, Shiqi; Xu, Yangsheng

doi:10.1109/wcica.2010.5554998

Cited by 16 publications

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The combination of both these feature provided better classification accuracy than using them individually. Guo et al [43] used Markov random fields for computing crowd density using three image features which are the optical flow, foreground detection and edge detection. These features were scaled so that objects far from the camera will be similar in size to objects near the camera.…”

Section: Density Estimationmentioning

confidence: 99%

Visual Surveillance for Hajj and Umrah: A Review

Ali

Simsim

2014

IJ-AI

View full text Add to dashboard Cite

This paper presents advances on crowd management research with specific interest on high density crowds such as Hajj and Umrah crowds. In the past few years, there has been increasing interest in pursuing video analytics and visual surveillance to improve the security and safety of pilgrimages during their stay in Mecca. Despite the fact that visual surveillance research has matured significantly in the rest of the world and had been implemented in many scenarios, research on visual surveillance for Hajj and Umrah application still remains at its early stages and there are many issues that need to be addressed in future research. This is mainly because Hajj is a very unique event that shows the clustering of millions of people in small area where most advanced image processing and computer vision algorithms fail to generate accurate analysis of the image content. There is a strong need to develop new algorithms specifically tailored for Hajj and Umrah applications. This review aims to give attentions to these interesting future research areas based on analysis of current visual surveillance research. The review also pinpoint to pioneer techniques on visual surveillance in general that can be customized to Hajj and Umrah applications.

show abstract

Section: Density Estimationmentioning

confidence: 99%

Visual Surveillance for Hajj and Umrah: A Review

Ali

Simsim

2014

IJ-AI

View full text Add to dashboard Cite

show abstract

“…The approaches above can work well in less crowded situations but not in overcrowded scenes. On the contrary, Guo et al [12] extracted three types of features from the optical flow, the foreground image and the edge image to estimate crowd density by employing Markov random field (MFR). Wang et al [13] propose a novel texture descriptor based on the local binary pattern co-occurrence matrix (LBPCM) on gray and gradient images for crowd density estimation.…”

Section: Related Workmentioning

confidence: 99%

Pedestrian Counting Based on Crowd Density Estimation and Lucas-Kanade Optical Flow

Zheng

Wang

2013

2013 Seventh International Conference on Image and Graphics

View full text Add to dashboard Cite

This paper proposes a novel method to estimate crowd densities in regions and count pedestrians passing through a virtual line. Firstly, the crowd density estimation for regions is based on regional feature analysis and support vector regression (SVR). We extract the following features from each segmented region: the pixel ratio and block-size histogram of the foreground, the pixel ratio and the Minkowski dimension of the edge image, and the gray-level co-occurrence matrix (GLCM) features of the gray image. SVR is used to train and estimate the crowd densities of regions with the extracted features. Secondly, we use the estimated crowd densities and the Lucas-Kanade (LK) optical flow to count pedestrians passing through the line. In each region, we divide the estimated crowd density by the number of foreground pixels and get the density per foreground pixel. Then, people moving speeds based on the LK optical flow are used to compute the number of foreground pixels crossing the line. After that, we can estimate the number of people passing through the line by multiplying the density per foreground pixel by the number of foreground pixels crossing the line. Each region is segmented into several small blocks for higher accuracy to compute the people moving speeds. The experimental results show that the proposed method has a good performance on both crowd density estimation and pedestrian counting.

show abstract

“…Crowd counting and density estimation research cannot only provide important guarantees for the safety of people's lives and property but also aid in promoting the maximization of social and economic benefits. It has a wide range of application prospects and important practical significance [13][14][15]. erefore, crowd counting and density estimation have gradually become a common research hotspot in academia and industry.…”

Section: Introductionmentioning

confidence: 99%

Crowd Density Estimation Method Using Deep Learning for Passenger Flow Detection System in Exhibition Center

Xiang¹,

Liu

2022

Scientific Programming

View full text Add to dashboard Cite

Aiming at the problems of crowd distribution, scale feature, and crowd feature extraction difficulties in exhibition centers, this paper proposes a crowd density estimation method using deep learning for passenger flow detection systems in exhibition centers. Firstly, based on the pixel difference symbol feature, the difference amplitude feature and gray feature of the central pixel are extracted to form the CLBP feature to obtain more crowd group description information. Secondly, use the LR activation function to add nonlinear factors to the convolution neural network (CNN) and use dense blocks derived from crowd density estimation to train the LR-CNN crowd density estimation model. Finally, experimental results show that the mean absolute error (MAE) and mean square error (MSE) of the proposed method in the UCF_CC_50 dataset are 325.6 and 369.4, respectively. Besides, MAE and MSE in part_A of the Shanghai Tech dataset are 213.5 and 247.1, respectively, and they in part_B are 85.3 and 99.7, respectively. The proposed method effectively improves the accuracy of crowd density estimation in exhibition centers.

show abstract

Crowd density estimation via Markov Random Field (MRF)

Cited by 16 publications

References 13 publications

Visual Surveillance for Hajj and Umrah: A Review

Visual Surveillance for Hajj and Umrah: A Review

Pedestrian Counting Based on Crowd Density Estimation and Lucas-Kanade Optical Flow

Crowd Density Estimation Method Using Deep Learning for Passenger Flow Detection System in Exhibition Center

Contact Info

Product

Resources

About