MCSHM: A simple and practical method for moving objects detection in dynamic scenes

Li, Jinping; Guo, Yukun; Ma, Liwen

doi:10.1109/cac.2017.8243687

Cited by 4 publications

(3 citation statements)

References 22 publications

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“…MCSHM first calculates statistical histograms of many color components in multiple color space and then use the variations of statistical histograms to find out whether there is an object. Thus, the computational complexity becomes very low [16]. 1) Relevance/Motivation: Our main aim is to implement multiple color space histogram model for detection of moving object from dynamic scenes.…”

Section: Methodology a Multiple Color Space Histogram Modelmentioning

confidence: 99%

“…Additionally, we use histogram superposition principle to capture the changes in histograms. In multiple color space, we utilize the range of each color component's histogram to determine if an object has appeared [16]. 2) Block Diagram: Multiple color space histogram model consists of combination of histograms in multiple color spaces and the superposition principle of statistical histogram.…”

Section: Methodology a Multiple Color Space Histogram Modelmentioning

confidence: 99%

“…Then, the variations in statistical histograms are used to detect whether there is an object and not the changes in pixels or pixel level regions. This expression can be interpreted as equaling H minus P plus R [16].…”

Section: Methodology a Multiple Color Space Histogram Modelmentioning

confidence: 99%

See 2 more Smart Citations

A Brief Review of Methods of Moving Object Detection, Challenges and Shortcomings

Shirke¹

2019

IJRASET

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Moving object detection from dynamic scenes has been used in many computer vision applications like face detection, video processing, video surveillance, traffic monitoring etc. Finding the position is much more challenging task than detecting the moving object in a video. Here we are presenting a brief review of various algorithms for moving object detection which are already available. Moving object detection from dynamic scenes using Multiple Color Space Histogram Model is briefly discussed in this paper. In this model, at first, convert each frame from RGB space to other color spaces and calculate the histograms of selected color components, then we can obtain the background histogram model; then, detect the objects using statistical histogram superposition principle; at last, update MCSHM by the result of detection. The experimental results demonstrate that our method can quickly and accurately detect moving objects in dynamic scenes.

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Section: Methodology a Multiple Color Space Histogram Modelmentioning

confidence: 99%

Section: Methodology a Multiple Color Space Histogram Modelmentioning

confidence: 99%