Dense crowd counting based on perspective weight model using a fisheye camera

Hu, Xuehai; Zheng, Hong; Chen, Yuzhang; Chen, Long

doi:10.1016/j.ijleo.2014.08.132

Cited by 14 publications

(10 citation statements)

References 16 publications

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“…The window A 0 is placed near the edge of the FoV, where pixel distances are smaller than other locations with high elevation φ and σ 0 is calculated according to Equation (8).…”

Section: Filter Kernels For Fish-eye Image Processing Based On the Gementioning

confidence: 99%

“…The initial Gaussian kernel g 0 is constructed according to Equation (9) with a stencil of 13 × 13. The standard deviation σ 0 of g 0 is calculated using Equation (8). A number of successive linear convolutions (*) is performed…”

Section: An Efficient Computational Implementation Of the Geodeticallmentioning

confidence: 99%

“…Fisheye cameras with special processing operators are proposed for large space monitoring and crowd counting in [8]. In [9], a network of fisheye cameras is used to efficiently track persons in a space with obstacles that hinder visibility.…”

Section: Introductionmentioning

confidence: 99%

“…The basic morphology operators have been redefined in [8], although the computational cost has not been discussed.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Implementation of Gaussian and Laplacian Kernels for Feature Extraction from IP Fisheye Cameras

Delibasis

2018

J. Imaging

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Abstract:The Gaussian kernel, its partial derivatives and the Laplacian kernel, applied at different image scales, play a very important role in image processing and in feature extraction from images. Although they have been extensively studied in the case of images acquired by projective cameras, this is not the case for cameras with fisheye lenses. This type of cameras is becoming very popular, since it exhibits a Field of View of 180 degrees. The model of fisheye image formation differs substantially from the simple projective transformation, causing straight lines to be imaged as curves. Thus the traditional kernels used for processing images acquired by projective cameras, are not optimal for fisheye images. This work uses the calibration of the acquiring fisheye camera to define a geodesic metric for distance between pixels in fisheye images and subsequently redefines the Gaussian kernel, its partial derivatives, as well as the Laplacian kernel. Finally, algorithms for applying in the spatial domain these kernels, as well as the Harris corner detector, are proposed, using efficient computational implementations. Comparative results are shown, in terms of correctness of image processing, efficiency of application for multi scale processing, as well as salient point extraction. Thus we conclude that the proposed algorithms allow the efficient application of standard processing and analysis techniques of fisheye images, in the spatial domain, once the calibration of the specific camera is available.

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“…The window A 0 is placed near the edge of the FoV, where pixel distances are smaller than other locations with high elevation φ and σ 0 is calculated according to Equation (8).…”

Section: Filter Kernels For Fish-eye Image Processing Based On the Gementioning

confidence: 99%

Section: An Efficient Computational Implementation Of the Geodeticallmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The basic morphology operators have been redefined in [8], although the computational cost has not been discussed.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Efficient Implementation of Gaussian and Laplacian Kernels for Feature Extraction from IP Fisheye Cameras

Delibasis

2018

J. Imaging

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“…For example, there are 5.9 million CCTV cameras in the UK [27]. People counting system is one of the most challenging systems in computer vision to implement [4], [21], [28]- [30]. In comparison with the conventional camera-based method, the problem with other technologies is that they need to be carefully planned and deployed for specific purposes.…”

mentioning

confidence: 99%

Image Processing Based Ambient Context-Aware People Detection and Counting

Habeeb¹,

Vuksanović²,

Habeeb³

2018

IJMLC

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Abstract-Different technologies are employed to detect and count people in various situations but crowd counting system based on computer vision is one of the best choices due to a number of advantages. These include accuracy, flexibility, cost and acquiring people distribution information. Crowd counting system based on computer vision can use closed circuit television cameras (CCTV) that have already become ubiquitous and their uses are increasing. This paper aims to develop crowd counting system that can be incorporated with existing CCTV cameras. In this paper, the extracted low-level features in a frame-to-frame analysis are processed using regression technique to estimate the number of people. Two complex scenes and environments are used to evaluate the performances of the proposed system. The results have shown that the proposed system can achieve good performance in terms of the mean absolute error (MAE) and mean squared error (MSE).

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A robust multimedia surveillance system for people counting

Habeeb

Ndzi

Kamarudin

et al. 2016

Multimed Tools Appl

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Closed circuit television cameras (CCTV) are widely used in monitoring. This paper presents an intelligent CCTV crowd counting system based on two algorithms that estimate the density of each pixel in each frame and use it as a basis for counting people. One algorithm uses scale-invariant feature transform (SIFT) features and clustering to represent pixels of frames (SIFT algorithm) and the other uses features from accelerated segment test (FAST) corner points with SIFT features (SIFT-FAST algorithm). Each algorithm is designed using a novel combination of pixel-wise, motion-region, grid map, background segmentation using Gaussian mixture model (GMM) and edge detection. A fusion technique is proposed and used to validate the accuracy by combining the result of the algorithms at frame level. The proposed system is more practical than the state of the art regression methods because it is trained with a small number of frames so it is relatively easy to deploy. In addition, it reduces the training error, setup time, cost and open the door to develop more accurate people detection methods. The University of California (UCSD) and Mall datasets have been used to test the proposed algorithms. The mean deviation error, mean squared error and the mean absolute error of the proposed system are less than 0.1, 16.5 and 3.1, respectively, for the Mall dataset and less than 0.07, 5.5 and 1.9, respectively, for UCSD dataset.

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Dense crowd counting based on perspective weight model using a fisheye camera

Cited by 14 publications

References 16 publications

Efficient Implementation of Gaussian and Laplacian Kernels for Feature Extraction from IP Fisheye Cameras

Efficient Implementation of Gaussian and Laplacian Kernels for Feature Extraction from IP Fisheye Cameras

Image Processing Based Ambient Context-Aware People Detection and Counting

A robust multimedia surveillance system for people counting

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