Edge Detection through Fuzzy System using Type I Format

Kaur, Gurjot; Virk, Ishpreet Singh

doi:10.5120/17877-8866

Cited by 1 publication

(1 citation statement)

References 8 publications

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“…There is a strong relationship between the complexity of crowds and the number of people because crowded environments tend to produce complex edges, while sparse environments tend to produce coarse edges [8]. Edges can be extracted using different algorithms such as Sobel, Canny, Prewitt, Roberts and Fuzzy logic algorithms [74], [75]. Canny edge detection is used in the proposed system.…”

Section: Edge Detectionmentioning

confidence: 99%

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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Section: Edge Detectionmentioning

confidence: 99%