Moving Object Detection using Lab2000HL Color Space with Spatial and Temporal Smoothing

Balcilar, Muhammet; Amasyalı, Mehmet Fatih; Sonmez, A. Coskun

doi:10.12785/amis/080433

Cited by 11 publications

(4 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…RGB is the most common color space used in BS. However, color spaces such as YUV, YCbCr and CIE L*a*b*, that separate the luminance component, have proven to be advantageous in image processing applications [7,8]. CIE L*a*b* is a perceptual color space, where the non-linear relationships for the L*, a*, and b* components are intended to mimic the Human Visual System features.…”

Section: Selection Of Color Spacementioning

confidence: 99%

BMOG: Boosted Gaussian Mixture Model with Controlled Complexity

Martins

Carvalho

Côrte-Real

et al. 2017

Pattern Recognition and Image Analysis

View full text Add to dashboard Cite

Developing robust and universal methods for unsupervised segmentation of moving objects in video sequences has proved to be a hard and challenging task. The best solutions are, in general, computationally heavy preventing their use in real-time applications. This research addresses this problem by proposing a robust and computationally efficient method, BMOG, that significantly boosts the performance of the widely used MOG2 method. The complexity of BMOG is kept low, proving its suitability for real-time applications. The proposed solution explores a novel classification mechanism that combines color space discrimination capabilities with hysteresis and a dynamic learning rate for background model update.

show abstract

Section: Selection Of Color Spacementioning

confidence: 99%

BMOG: Boosted Gaussian Mixture Model with Controlled Complexity

Martins

Carvalho

Côrte-Real

et al. 2017

Pattern Recognition and Image Analysis

View full text Add to dashboard Cite

show abstract

“…The conclusion is that the Lab2000HL color space increases foreground detection rate significantly, in spite of its high computational cost. Balcilar et al [25] improved the performance obtained by the Lab2000HL color space with a spatial and temporal smoothing scheme.…”

Section: Features In Well-known Color Spacesmentioning

confidence: 99%

On the Role and the Importance of Features for Background Modeling and Foreground Detection

Bouwmans¹,

Silva²,

Marghes³

et al. 2016

Preprint

View full text Add to dashboard Cite

Background modeling has emerged as a popular foreground detection technique for various applications in video surveillance. Background modeling methods have become increasing efficient in robustly modeling the background and hence detecting moving objects in any visual scene. Although several background subtraction and foreground detection have been proposed recently, no traditional algorithm today still seem to be able to simultaneously address all the key challenges of illumination variation, dynamic camera motion, cluttered background and occlusion. This limitation can be attributed to the lack of systematic investigation concerning the role and importance of features within background modeling and foreground detection. With the availability of a rather large set of invariant features, the challenge is in determining the best combination of features that would improve accuracy and robustness in T. Bouwmans Lab. MIA,

show abstract

“…To improve the performance, researchers also propose to use descriptors. Some researchers transform the pixels from RGB space into other color spaces to separate color intensity from other color information ( Balcilar, Amasyali & Sonmez, 2014 ; Martins et al, 2017 ). Another effective descriptor for background subtraction is texture-based local binary pattern (LBP) ( Heikkila & Pietikainen, 2006 ).…”

Section: Introductionmentioning

confidence: 99%

Background subtraction for night videos

Pan

Zhu

Peng

et al. 2021

PeerJ Computer Science

View full text Add to dashboard Cite

Motion analysis is important in video surveillance systems and background subtraction is useful for moving object detection in such systems. However, most of the existing background subtraction methods do not work well for surveillance systems in the evening because objects are usually dark and reflected light is usually strong. To resolve these issues, we propose a framework that utilizes a Weber contrast descriptor, a texture feature extractor, and a light detection unit, to extract the features of foreground objects. We propose a local pattern enhancement method. For the light detection unit, our method utilizes the finding that lighted areas in the evening usually have a low saturation in hue-saturation-value and hue-saturation-lightness color spaces. Finally, we update the background model and the foreground objects in the framework. This approach is able to improve foreground object detection in night videos, which do not need a large data set for pre-training.

show abstract

Moving Object Detection using Lab2000HL Color Space with Spatial and Temporal Smoothing

Cited by 11 publications

References 38 publications

BMOG: Boosted Gaussian Mixture Model with Controlled Complexity

BMOG: Boosted Gaussian Mixture Model with Controlled Complexity

On the Role and the Importance of Features for Background Modeling and Foreground Detection

Background subtraction for night videos

Contact Info

Product

Resources

About