Human Detection and Motion Tracking Using Machine Learning Techniques: A Review

Mahajan, Rohini; Padha, Devanand

doi:10.1109/pdgc.2018.8745852

Cited by 5 publications

(1 citation statement)

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“…The subtraction process cannot handle the fast moving objects because when the object in motion goes still or moves fast, for a few frames, background and foreground can't be distinguished. Also, thresholding the difference is a kind of exacting approach which may lead to unnoticed activity of the interest of researcher (Anandhalli & Baligar, 2015) (Mahajan & Padha, 2018). The widespread modus operandi for distinguishing the background from the foreground is Background Subtraction (BS) which extracts low entropy based pixels of the object in motion without any prior information about the scene proving to be effectual in stationary camera arrangements and highly precise in pixel, frame as well as region level procedure (Kumar & Yadav, 2016b).…”

Section: Introductionmentioning

confidence: 99%

Detection of Change in Body Motion With Background Construction and Silhouette Orientation

Mahajan

Padha

2022

International Journal of Information Retrieval Research

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Background subtraction techniques have been widely implemented and improvised to obtain a stable background model. The novelty of the proposed work is to generate a stable background model under dynamic changes in the environmental conditions where a) an improved background subtraction algorithm is proposed based on GMM with EM algorithm for computing granulometry and run faster for the generation of a stable background model; b) Detecting the foreground by curvelet based denoising process with improvised semisoft thresholding techniques with morphological operations; c) background maintenance is done by an adaptive algorithm in which the intensity values are mapped to remove the connected components with less than P pixels. The proposed scheme works for the Spatio-temporal motion of the object in both spatial and temporal modes. The experimental outcome for the proposed model results in the accurate shape analysis of the object in motion thereby dipping the complexity

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Section: Introductionmentioning

confidence: 99%