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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/5647
Title: Moving object detection using modified temporal differencing and local fuzzy thresholding
Authors: Paul N.
Singh A.
Midya A.
Pratim Roy, Partha
Dogra D.P.
Published in: Journal of Supercomputing
Abstract: Most of the existing video object detection schemes are either computationally extensive or fail to detect moving objects in different challenging situations. In this paper, we propose a robust and computationally inexpensive scheme to detect moving objects in video. The threefold approach begins with computation of difference images using temporal information. Difference images are calculated by subtracting two input frames, at each pixel position. Instead of generating difference images using the traditional continuous frame difference approach, we propose using a fixed number of alternate frames centered around the current frame. This approach aids in reducing the computational complexity without compromising on quality of the difference images. After computation of difference images, a novel post-processing scheme is employed by utilizing gamma correction factor and Mahalanobis distance metric to reduce false positives and false negatives. Object segmentation is finally performed on the refined difference image by a local fuzzy thresholding scheme. This avoids problems that are usually encountered in hard thresholding, especially pixel misclassification, which is the most important one. For robust experimental analysis, videos from changedetction.net, CAVIAR, and http://perception.i2r datasets have been used. These selected videos contain a wide variety of common challenges faced during object detection. Some examples are the presence of dynamic backgrounds, shadows, bad weather, etc. The results establish the effectiveness of the proposed scheme over some of the existing schemes both qualitatively and quantitatively as delineated in the experimental result section. © 2016, Springer Science+Business Media New York.
Citation: Journal of Supercomputing (2017), 73(3): 1120-1139
URI: https://doi.org/10.1007/s11227-016-1815-7
http://repository.iitr.ac.in/handle/123456789/5647
Issue Date: 2017
Publisher: Springer New York LLC
Keywords: Difference image
Dynamic background
Fuzzy thresholding
Ghosting
Moving object detection
Temporal information
ISSN: 9208542
Author Scopus IDs: 57095489100
57212846820
53863949900
56880478500
35408975400
Author Affiliations: Paul, N., Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Silchar, India
Singh, A., Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Silchar, India
Midya, A., Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Silchar, India
Roy, P.P., Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, India
Dogra, D.P., School of Electrical Sciences, Indian Institute of Technology Bhubaneswar, Bhubaneswar, India
Corresponding Author: Singh, A.; Department of Electronics and Communication Engineering, National Institute of Technology SilcharIndia; email: ashish090494@gmail.com
Appears in Collections:Journal Publications [CS]

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