Robust moving object detection against fast illumination change

Choi, JinMin; Chang, Hyung Jin; Yoo, Yeon-Jee; Choi, Jin Young

doi:10.1016/j.cviu.2011.10.007

Cited by 39 publications

(12 citation statements)

References 22 publications

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“…Finally, we show that the proposed method rapidly adapts to variations in environment. To test moving object detection under illumination changes condition, we compare the result of other techniques which are GMM [5], adaptive GMM [24] that controls the learning rate of the GMM adaptively, LBP [25] that detects moving objects using texture and edges, Xu's methods [26] that use a color chromaticity, Pilet's methods [27] that use illumination and spatial likelihood, and Choi's methods [28] that develop chromaticity difference model and brightness model that estimates the intensity difference and intensity ratio of false foreground pixels. For the quantitative comparison, we have made three test videos [28].…”

Section: Experiments Resultsmentioning

confidence: 99%

Moving Object Detection and Shadow Removing under Changing Illumination Condition

Xiang

Fan

Liao

et al. 2014

Mathematical Problems in Engineering

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Moving object detection is a fundamental step in video surveillance system. To eliminate the influence of illumination change and shadow associated with the moving objects, we proposed a local intensity ratio model (LIRM) which is robust to illumination change. Based on the analysis of the illumination and shadow model, we discussed the distribution of local intensity ratio. And the moving objects are segmented without shadow using normalized local intensity ratio via Gaussian mixture model (GMM). Then erosion is used to get the moving objects contours and erase the scatter shadow patches and noises. After that, we get the enhanced moving objects contours by a new contour enhancement method, in which foreground ratio and spatial relation are considered. At last, a new method is used to fill foreground with holes. Experimental results demonstrate that the proposed approach can get moving objects without cast shadow and shows excellent performance under various illumination change conditions.

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Section: Experiments Resultsmentioning

confidence: 99%

Moving Object Detection and Shadow Removing under Changing Illumination Condition

Xiang

Fan

Liao

et al. 2014

Mathematical Problems in Engineering

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“…A robust moving object detection algorithm must handle the nonidealities of scenes such as changes in illumination, high frequency motion, changes of longterm scene, and also shadows. For the past decade, numerous algorithms were proposed to deal with the above mentioned problems [3,4,5]. Computer vision plays a very important role in the development of video surveillance technology.…”

Section: Introductionmentioning

confidence: 99%

Multiple vehicles detection and tracking for intelligent transport systems using machine learning approaches

Bui

Lai

Hieu

et al. 2019

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Video surveillance is emerging research field of intelligent transport systems. This paper presents some techniques which use machine learning and computer vision in vehicles detection and tracking. Firstly the machine learning approaches using Haar-like features and Ada-Boost algorithm for vehicle detection are presented. Secondly approaches to detect vehicles using the background subtraction method based on Gaussian Mixture Model and to track vehicles using optical flow and multiple Kalman filters were given. The method takes advantages of distinguish and tracking multiple vehicles individually. The experimental results demonstrate high accurately of the method.

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“…In order to overcome the problems of apertures and discontinuities in segmentation, researchers [3][4][5][6] tried to improve the robustness of background subtraction algorithm for the illumination changes. Nevertheless, few researches worked on the apertures and discontinuities from the view of color model used by images [7].…”

Section: Introductionmentioning

confidence: 99%