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RETRACTED ARTICLE: Video analytics for semantic substance extraction using OpenCV in python

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This article was retracted on 04 July 2022

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Abstract

With the rise of crimes all over the world, video surveillance is gaining more significance day by day. Presently, monitoring videos is done manually. If a crime occurs in a city, to find the sequence or event, it is necessary to play the entire video after which searching and processing needs to be done manually. Due to the lack of human resource, it is necessary to develop a new video analytics framework to perform higher level tasks in semantic content extraction. Manual processing of video is wasteful, one-sided, and more expensive thereby limiting the searching abilities. So it is necessary to model a framework for extracting objects from the video data. A Semantic Substance Extraction model using OpenCV is proposed for organizing video resources. Video analytics for semantic substance extraction is an effort to use real time, publicly available data to improve the prediction of the moving objects from the video streams. Background separation and Haar Cascade algorithms are used in this model to perform video analytics. Usage of this method has achieved a detection precision of 84.11% and a recall of 50.27%. These results are 78% faster than content extraction using existing fuzzy and neural methods.

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Authors and Affiliations

  1. Saveetha School of Engineering, SIMATS, Chennai, India

    A. Manju

  2. Saveetha Engineering College, Chennai, India

    A. Manju

  3. Department of Computer Science and Engineering, Saveetha Engineering College, Chennai, India

    P. Valarmathie

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  1. A. Manju
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  2. P. Valarmathie
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Correspondence to A. Manju.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12652-022-04272-3"

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Manju, A., Valarmathie, P. RETRACTED ARTICLE: Video analytics for semantic substance extraction using OpenCV in python. J Ambient Intell Human Comput 12, 4057–4066 (2021). https://doi.org/10.1007/s12652-020-01780-y

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  • DOI: https://doi.org/10.1007/s12652-020-01780-y

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