NDLI: Highly efficient neoteric histogram–entropy-based rapid and automatic thresholding method for moving vehicles and pedestrians detection

Content Provider	IET Digital Library
Author	Chandrasekar, Karnam Silpaja Geetha, Planisamy
Abstract	Thresholding for segmentation is an important key step and necessary process in various applications. Estimating an accurate threshold value for a complex and coarse image is computationally expensive and lacks accuracy and stability. This study is aimed at developing a general histogram–entropy-based thresholding method, referred as our HEBT method, for fast and efficient automatic threshold value evaluation. In the proposed method, the probability density function and Shannon entropy derived from 1D bimodal histogram have been used to find the optimal threshold values automatically. The proposed method implemented with a three-frame differencing segmentation technique has been tested on real-time datasets – change detection 2012, change detection 2014, and Wallflower – to identify pedestrians and vehicles. The performance of our HEBT method has been compared with six state-of-the-art automatic thresholding methods. The experimental segmented image results confirmed that our HEBT method is more adaptable and better suited for real-time systems with severe challenging conditions of great variations. Further, the new HEBT method achieved the best segmentation results with highest values of several performance parameters, i.e. recall, precision, similarity, and f-measure. Interestingly, the computation time is the lowest for the proposed method than the state-of-the-art methods, promising its application for a fast and effective image segmentation.
Starting Page	354
Ending Page	365
Page Count	12
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 2, Feb (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5555
Journal	IET Image Processing
Publisher Date	2019-10-21
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	1D Bimodal Histogram Accurate Threshold Value Automatic Thresholding Method Change Detection Coarse Image Complex Image Computer Vision And Image Processing Technique Effective Image Segmentation Efficient Automatic Threshold Value Evaluation Entropy Experimental Segmented Image Result Fast Image Segmentation Fast Threshold Value Evaluation General Histogram–entropy-based Thresholding Method HEBT Method Image Segmentation Important Key Step Object Detection Optical, Image And Video Signal Processing Optimal Threshold Value Pedestrian Detection Probability State-of-the-art Automatic Thresholding Method Three-frame Differencing Segmentation Technique Vehicle Video Signal Processing Video Surveillance
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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