Crowd Density Estimation Based on Texture Feature Extraction

Wang, Bobo; Bao, Hong; Yang, Shan; Lou, Haitao

doi:10.4304/jmm.8.4.331-337

Cited by 15 publications

(11 citation statements)

References 11 publications

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“…Gray-level cooccurrence matrix and local binary pattern are usually used to find texture features [5], [62], [63]. Texture features include homogeneity (texture smoothness), energy (total sumsquared energy), entropy (texture randomness) and contrast [5], [37], [60].…”

Section: Feature Representation and Selectionmentioning

confidence: 99%

An adaptive people counting system with dynamic features selection and occlusion handling

Habeeb

Ndzi

Yang

et al. 2016

Journal of Visual Communication and Image Representation

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This paper presents an adaptive crowd counting system for video surveillance applications. The proposed method is composed of a pair of collaborative Gaussian process models (GP) with different kernels, which are designed to count people by taking the level of occlusion into account. The level of occlusion is measured and compared with a predefined threshold for regression model selection for each frame. In addition, the proposed method dynamically identifies the best combination of features for people counting. The Mall and UCSD datasets are used to evaluate the proposed method. The results show that the proposed method offers a higher accuracy when compared against state of the art methods reported in open literature. The mean absolute error (MAE), mean squared error (MSE) and the mean deviation error (MDE) for the proposed algorithm are 2.90, 13.70 and 0.095, respectively, for the Mall dataset and 1.63, 4.32 and 0.066, respectively, for UCSD dataset.

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Section: Feature Representation and Selectionmentioning

confidence: 99%

An adaptive people counting system with dynamic features selection and occlusion handling

Habeeb

Ndzi

Yang

et al. 2016

Journal of Visual Communication and Image Representation

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“…The study, however, has not provided sufficient results to prove the effectiveness of the proposed method. Wang et al (2013) proposed a new approach for crowd density estimation through using a new texture feature called Tamura. Crowd features were extracted based on the grey level co-occurrence matrix (GLCM).…”

Section: Related Workmentioning

confidence: 99%

Estimation of crowd density from UAVs images based on corner detection procedures and clustering analysis

Almagbile

2018

Geo-spatial Information Science

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With rapid developments in platforms and sensors technology in terms of digital cameras and video recordings, crowd monitoring has taken a considerable attentions in many disciplines such as psychology, sociology, engineering, and computer vision. This is due to the fact that, monitoring of the crowd is necessary to enhance safety and controllable movements to minimize the risk particularly in highly crowded incidents (e.g. sports). One of the platforms that have been extensively employed in crowd monitoring is unmanned aerial vehicles (UAVs), because UAVs have the capability to acquiring fast, low costs, high-resolution and real-time images over crowd areas. In addition, geo-referenced images can also be provided through integration of on-board positioning sensors (e.g. GPS/IMU) with vision sensors (digital cameras and laser scanner). In this paper, a new testing procedure based on feature from accelerated segment test (FAST) algorithms is introduced to detect the crowd features from UAV images taken from different camera orientations and positions. The proposed test started with converting a circle of 16 pixels surrounding the center pixel into a vector and sorting it in ascending/descending order. A single pixel which takes the ranking number 9 (for FAST-9) or 12 (for FAST-12) was then compared with the center pixel. Accuracy assessment in terms of completeness and correctness was used to assess the performance of the new testing procedure before and after filtering the crowd features. The results show that the proposed algorithms are able to extract crowd features from different UAV images. Overall, the values of Completeness range from 55 to 70 % whereas the range of correctness values was 91 to 94 %.

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“…In general, texture feature and a classifier are used to estimate the crowd density. It introduced the grey level co-occurrence matrix(GLCM) of an image [3,4]. Then the support vector machine (SVM)is used to analyse crowd density [4].…”

Section: Introductionmentioning

confidence: 99%

“…It introduced the grey level co-occurrence matrix(GLCM) of an image [3,4]. Then the support vector machine (SVM)is used to analyse crowd density [4]. The feature is effect when the crowd is very dense.…”

Section: Introductionmentioning

confidence: 99%

An Improved Convolutional Neural Network on Crowd Density Estimation

2016

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Abstract.In this paper, a new method is proposed for crowd density estimation. An improved convolutional neural network is combined with traditional texture feature. The data calculated by the convolutional layer can be treated as a new kind of features.So more useful information of images can be extracted by different features.In the meantime, the size of image has little effect on the result of convolutional neural network. Experimental results indicate that our scheme has adequate performance to allow for its use in real world applications.

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Crowd Density Estimation Based on Texture Feature Extraction

Cited by 15 publications

References 11 publications

An adaptive people counting system with dynamic features selection and occlusion handling

An adaptive people counting system with dynamic features selection and occlusion handling

Estimation of crowd density from UAVs images based on corner detection procedures and clustering analysis

An Improved Convolutional Neural Network on Crowd Density Estimation

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