Robust foreground detection in sudden illumination change

Mahmoudpour, Saeed; Kim, M.

doi:10.1049/el.2015.4077

Cited by 7 publications

(6 citation statements)

References 5 publications

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“…We evaluated the proposed method in six video sequences of various illumination conditions. The light switch and time of day video sequences from Wallflower dataset [9], people movement video sequences from PETS 2001 dataset [10], lobby video from I2R dataset [11] and highway and pedestrians video sequences from changedetection.net dataset [12] are used to compare the proposed algorithm with SG [5], GMM [6], ISBS [7] and DBIC [8] algorithms. We have taken our test video sequences in the order of three indoor and three outdoor videos.…”

Section: Resultsmentioning

confidence: 99%

“…The existing algorithms [4] can handle either the problem of gradual illumination changes or sudden illumination changes. The well-known methods like Single Gaussian (SG) [5] and Gaussian Mixture Model (GMM) [6] mostly adapt to gradual illumination changes, whereas Dual Background Illumination Compensator (DBIC) [7] and Illumination-Sensitive Background Subtraction (ISBS) [8] methods mostly adapt to sudden illumination variations. We proposed an algorithm to handle both sudden and gradual illumination changes.…”

Section: Introductionmentioning

confidence: 99%

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Entropy Based Illumination-Invariant Foreground Detection

Rajamanickam

Sakthivel

2019

IEICE Trans. Inf. & Syst.

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Background subtraction algorithms generate a background model of the monitoring scene and compare the background model with the current video frame to detect foreground objects. In general, most of the background subtraction algorithms fail to detect foreground objects when the scene illumination changes. An entropy based background subtraction algorithm is proposed to address this problem. The proposed method adapts to illumination changes by updating the background model according to differences in entropy value between the current frame and the previous frame. This entropy based background modeling can efficiently handle both sudden and gradual illumination variations. The proposed algorithm is tested in six video sequences and compared with four algorithms to demonstrate its efficiency in terms of F-score, similarity and frame rate.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Entropy Based Illumination-Invariant Foreground Detection

Rajamanickam

Sakthivel

2019

IEICE Trans. Inf. & Syst.

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“…The parameters of the model may improve or weaken the performance of the background subtraction method in different environments. The matching threshold R , the minimum number of matches' threshold Th and the number of background samples N from (8), (10) and (12) were studied in this subsection. R and Th need to be adjusted in different environments to obtain the optimal performance, which is a challenging work.…”

Section: Bparameters Settingmentioning

confidence: 99%

“…An illumination evaluation is used to analyze illumination changes and determine light background and dark background candidates [9]. Two background models with different adaption rates were utilized to address the updating of the model in sudden illumination changes [10]. Recently, an illumination change model, a chromaticity difference model and a brightness ratio model were developed to deal with fast illumination changes in a visual surveillance system [11].…”

Section: Introductionmentioning

confidence: 99%

Background Subtraction Using an Adaptive Local Median Texture Feature in Illumination Changes Urban Traffic Scenes

et al. 2020

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Background subtraction is commonly employed in foreground object detection in urban traffic scenes. Most of the current color or texture feature-based background subtraction models are easily contaminated by sudden and gradual illumination variations in urban traffic scenes. To resolve this deficiency, an adaptive local median texture feature, which extracts the adaptive distance threshold employing the median information in a predefined local region of a pixel and Weber's law, is introduced. In addition, a sample consensus-based model that evolved from portable visual background extractor is proposed using an adaptive local median texture feature. Then, the foreground is labeled by comparing the input video frames feature with the model. Moreover, to adapt the dynamic background, the random update scheme is used to update the model. Extensive experimental results on the public Change Detection data set of 2014 (CDnet2014) and the real-world urban traffic videos demonstrate that our background subtraction method is superior to the other state-of-the-art texture-feature-based methods. The qualitative and quantitative results show the encouraging efficiency of the proposed technique to deal with sudden and gradual illumination variations in real-world urban traffic scenes. INDEX TERMS background modeling, illumination variations, local median texture feature, urban traffic scenes

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“…A two‐layer GMM optimised using a Markov random field decision framework was proposed to represent the background of different lighting conditions [7], and the illumination evaluation using the entropy theory was suggested to build the light and dark background models [8]. Two background models with slow and fast adaption rates for accurate illumination compensation were utilised to address the illumination change problem [9]. Three‐layer models of illumination change, chromaticity difference and brightness were developed to detect moving objects against fast illumination change [10].…”

Section: Introductionmentioning

confidence: 99%