scholarly journals Feature Descriptor for Crowd Density Estimation

2019 ◽  
Author(s):  
Muhammad Bilal ◽  
Adwan Alanazi
Keyword(s):  
Density Estimation ◽  
State Of The Art ◽  
Texture Feature ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Descriptor ◽  
Crowd Density Estimation ◽  
Crowd Density ◽  
Feature Based ◽  
Crowd Monitoring

Crowd density estimation is an important task for crowd monitoring. Many efforts have been done to automate the process of estimating crowd density from images and videos. Despite series of efforts, it remains a challenging task. In this paper, we proposes a new texture feature-based approach for the estimation of crowd density based on Completed Local Binary Pattern (CLBP). We first divide the image into blocks and then re-divide the blocks into cells. For each cell, we compute CLBP and then concatenate them to describe the texture of the corresponding block. We then train a multi-class Support Vector Machine (SVM) classifier, which classifies each block of image into one of four categories, i.e. Very Low, Low, Medium, and High. We evaluate our technique on the PETS 2009 dataset, and from the experiments, we show to achieve 95% accuracy for the proposed descriptor.  We also compare other state-of-the-art texture descriptors and from the experimental results, we show that our proposed method outperforms other state-of-the-art methods.

MULTI-RESOLUTION CROWD DENSITY ESTIMATION BASED ON TEXTURE ANALYSIS AND LEARNING FROM DEMONSTRATION

2007 ◽  
Vol 04 (01) ◽  
pp. 1-14 ◽  
Author(s):  
GUOYUAN LIANG ◽  
KA KEUNG LEE ◽  
YANGSHENG XU
Keyword(s):  
Texture Analysis ◽  
Density Estimation ◽  
Texture Feature ◽  
Texture Features ◽  
Input Image ◽  
Support Vector ◽  
Surveillance Systems ◽  
Regression Problem ◽  
Crowd Density Estimation ◽  
Crowd Density

Crowd density estimation is very important for intelligent surveillance systems in public places. This paper presents an automatic method of estimating crowd density using texture analysis and machine learning. First the crowd scene is modeled as a series of multi-resolution image cells based on perspective projection. The cell size is normalized to obtain a uniform representation of texture features. Then the feature vectors of textures are extracted from each input image cell and the support vector machine (SVM) method is utilized to solve the regression problem for calculating the crowd density. In order to diminish the instability of texture feature measurements, a technique of searching the extrema in the Harris–Laplacian space is applied. Finally, the SVM method is used again to detect some abnormal situations caused by the changes in density distribution. Experiments on real crowd videos show the effectiveness of the proposed system.

scholarly journals Crowd Density Estimation of Scenic Spots Based on Multifeature Ensemble Learning

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaohang Xu ◽  
Dongming Zhang ◽  
Hong Zheng
Keyword(s):  
Density Estimation ◽  
Ensemble Learning ◽  
Human Head ◽  
Support Vector ◽  
Head Width ◽  
Social Stability ◽  
Classification Rate ◽  
Camera Angle ◽  
Crowd Density Estimation ◽  
Crowd Density

Estimating the crowd density of public territories, such as scenic spots, is of great importance for ensuring population safety and social stability. Due to problems in scenic spots such as illumination change, camera angle change, and pedestrian occlusion, current methods are unable to make accurate estimations. To deal with these problems, an ensemble learning (EL) method using support vector regression (SVR) is proposed in this study for crowd density estimation (CDE). The method first uses human head width as a reference to separate the foreground into multiple levels of blocks. Then it adopts the first-level SVR model to roughly predict the three features extracted from image blocks, including D-SIFT, ULBP, and GIST, and the prediction results are used as new features for the second-level SVR model for fine prediction. The prediction results of all image blocks are added for density estimation according to the crowd levels predefined for different scenes of scenic spots. Experimental results demonstrate that the proposed method can achieve a classification rate over 85% for multiple scenes of scenic spots, and it is an effective CDE method with strong adaptability.

scholarly journals Congested Crowd Counting via Adaptive Multi-Scale Context Learning

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3777
Author(s):  
Yani Zhang ◽  
Huailin Zhao ◽  
Zuodong Duan ◽  
Liangjun Huang ◽  
Jiahao Deng ◽  
...  
Keyword(s):  
Density Estimation ◽  
State Of The Art ◽  
Spatial Context ◽  
Context Information ◽  
Crowd Counting ◽  
Multi Scale ◽  
Context Learning ◽  
Crowd Density Estimation ◽  
Crowd Density ◽  
Density Results

In this paper, we propose a novel congested crowd counting network for crowd density estimation, i.e., the Adaptive Multi-scale Context Aggregation Network (MSCANet). MSCANet efficiently leverages the spatial context information to accomplish crowd density estimation in a complicated crowd scene. To achieve this, a multi-scale context learning block, called the Multi-scale Context Aggregation module (MSCA), is proposed to first extract different scale information and then adaptively aggregate it to capture the full scale of the crowd. Employing multiple MSCAs in a cascaded manner, the MSCANet can deeply utilize the spatial context information and modulate preliminary features into more distinguishing and scale-sensitive features, which are finally applied to a 1 × 1 convolution operation to obtain the crowd density results. Extensive experiments on three challenging crowd counting benchmarks showed that our model yielded compelling performance against the other state-of-the-art methods. To thoroughly prove the generality of MSCANet, we extend our method to two relevant tasks: crowd localization and remote sensing object counting. The extension experiment results also confirmed the effectiveness of MSCANet.

Crowd density estimation method based on floor area

2020 ◽  
Vol 1651 ◽  
pp. 012060
Author(s):  
Fujian Feng ◽  
Shuang Liu ◽  
Yongzheng Pan ◽  
Xin He ◽  
Jiayin Wei ◽  
...  
Keyword(s):  
Density Estimation ◽  
Estimation Method ◽  
Floor Area ◽  
Crowd Density Estimation ◽  
Crowd Density

Crowd Density Estimation Using Fusion of Multi-Layer Features

2020 ◽  
pp. 1-12
Author(s):  
Xinghao Ding ◽  
Fujin He ◽  
Zhirui Lin ◽  
Yu Wang ◽  
Huimin Guo ◽  
...  
Keyword(s):  
Density Estimation ◽  
Crowd Density Estimation ◽  
Crowd Density
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