Background subtraction with neighbor-based intensity correction algorithm

Huynh‐The, Thien; Baños, Oresti; Le, Ba-Vui; Bui, Dinh-Mao; Lee, Sungyoung; Yoon, Yongik; Le-Tien, Thuong

doi:10.1109/atc.2015.7388331

Cited by 1 publication

(2 citation statements)

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“…This video sequences have good crowded properties and challenging variations such as both walking and running people, illumination changes and diverse crowd densities and sizes. This sequence also has the ground truth annotations [11].…”

Section: Resultsmentioning

confidence: 99%

“…Despite the high performance in real-time environments, the drawbacks of the codebook approach include long periods of time for creating models and using large memory for storing code words. Another highly used nonparametric method in subtracting the background is the kernel density estimate (KDE) [11]. This method uses a histogram to evaluate the probability density function to override the current value background pixels.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

New Insights of Background Estimation and Region Localization

Lin

2019

IJRE

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Subtraction of background in a crowded scene is a crucial and challenging task of monitoring the surveillance systems. Because of the similarity between the foreground object and the background, it is known that the background detection and moving foreground objects is difficult. Most of the previous works emphasize this field but they cannot distinguish the foreground from background due to the challenges of gradual or sudden illumination changes, high-frequencies background objects of motion changes, background geometry changes and noise. After getting the foreground objects, segmentation is need to localize the objects region. Image segmentation is a useful tool in many areas, such as object recognition, image processing, medical image analysis, 3D reconstruction, etc. In order to provide a reliable foreground image, a carefully estimated background model is needed. To tackle the issues of illumination changes and motion changes, this paper establishes an effective new insight of background subtraction and segmentation that accurately detect and segment the foreground people. The scene background is investigates by a new insight, namely Mean Subtraction Background Estimation (MS), which identifies and modifies the pixels extracted from the difference of the background and the current frame. Unlike other works, the first frame is calculated by MS instead of taking the first frame as an initial background. Then, this paper make the foreground segmentation in the noisy scene by foreground detection and then localize these detected areas by analyzing various segmentation methods. Calculation experiments on the challenging public crowd counting dataset achieve the best accuracy than state-of-the-art results. This indicates the effectiveness of the proposed work.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%