A Method for Tracking Vehicles Under Occlusion Problem
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Tracking (education)
Subtraction
Foreground detection
Background image
Similarity (geometry)
Subtraction
Sequence (biology)
Image subtraction
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An increasing number of CCTV have been deployed in public and crime-prone areas as demand for automatic monitoring system is increasing to counterbalance the limitation of human monitoring. To have a good monitoring system in such places, a good background model is needed in order to reduce amount of the video processing needed for tracking, classification, counting and etc. This paper proposes an adaptive background modeling that is able to model a scene under review at real-time. The proposed modeling system is also expected to be able to handle dynamic backgrounds and common problems in detection methods. A novel patch-based background reconstruction based on highest frequency of occurrences assumption and past pixel observation is proposed. Contrast adjusting method is used to reduce the problem of incorrectly classified foreground which is shadow problem. The proposed algorithm is focused to be tested and analytically compared with the dynamic background at the indoor and outdoor environment. The main challenges of background subtraction such as illumination changes, geometrical changes, stationary moving object problem and high speed object problem are taken care of and extensively discussed in this paper. The experimental results show that the algorithm is able to reconstruct a background model and produce accurate and precise foreground that can be used for other processing stages.
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Tracking (education)
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We propose a robust method to extract silhouettes of foreground objects from color video sequences. To cope with various changes in the background, the background is modeled as generalized Gaussian Family of distributions and updated by the selective running average and static pixel observation. All pixels in the input video image are classified into four initial regions using background subtraction with multiple thresholds, after which shadow regions are eliminated using color components. The final foreground silhouette is extracted by refining the initial region using morphological processes. We have verified that the proposed algorithm works very well in various background and foreground situations through experiments. Keyword Foreground segmentation, Silhouette extraction, Background subtraction, Generalized Gaussian Family model
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Robust shadow removal algorithm for accurate object detection based on background subtraction method
This paper presents a new shadow removal algorithm for accurate object detection based on the background subtraction method. The algorithm requires no threshold to determine shadow regions, whereas the conventional algorithms suffer from specifying the thresholds for various illumination conditions. It finds shadow regions by means of measuring relative similarities between shadow candidate regions and a background image given by the BSM. We empirically verify that it is very effective to remove the shadow regions.
Image subtraction
Subtraction
Shadow mapping
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This paper describes an action research that aimed at improving pupils' performance in doing subtraction with regrouping of 1-digit numbers from 2-digit numbers and 2-digit numbers from 2-digit numbers. This study involved six Year 4 Malaysian Primary school pupils who were selected from 22 pupils who had sat for a test consisting of questions on subtraction without and with regrouping. The six pupils were found to be able to do subtraction without regrouping problems but had difficulties doing subtraction with regrouping problems. The study examines the effectiveness of the method called "Shortcut for Subtraction" in improving pupils' skills in doing subtraction with regrouping problems. The "Shortcut for Subtraction" method was the method used to replace the traditional "borrowing" method which was used initially to teach the pupils to do subtraction with regrouping.
Subtraction
Numerical digit
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Background subtraction, which separates moving objects in video sequences, is an essential technology for object recognition and tracking. However, background subtraction methods are often confused by shadow regions and this misclassification of shadow regions disturbs further processes to perceive the shapes or exact positions of moving objects. This paper proposes a method for shadow elimination which is based on shadow modeling by color information and Bayesian classification framework. Also, because of dynamic update of modeling parametres, the proposed method is able to correspond adaptively to illumination changes. Experimental results proved that the proposed method can eliminate shadow regions effectively even for circumstances with varying lighting condition.
Tracking (education)
Subtraction
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Moving Object detection and tracking in a video have applications in video-surveillance and robotics, human-computer interaction. Three frame differencing is better than two frames difference technique due to fewer problems of holes. Dynamic background detection technique is much better than static background technique for video with background change. So in this paper, background is updated with averaging of frame t-1, frame t+1 and previous updated background. This updated background is subtracted from frame t for foreground detection and merged with three frame subtraction. So there is scope of work such that holes problem should be reduced more and object should be detected better in dynamic changes in background. In this work, the proposed technique is able to reduce the holes problem in dynamic background updating video. This technique is extract foreground better than existing static and dynamic background.
Foreground detection
Tracking (education)
Background image
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As urban road intersections are prone to traffic congestion and traffic accidents, monitoring the crossing of vehicles and predicting the state is needed to reduce traffic congestion, regulate driver behavior and prevent accidents. Background subtraction and mean shift tracking are used to track vehicles. The whole monitoring process is as following. Firstly, secondary selected strategy is used to construct background model. Then vehicle tracking objects are built at the trigger area of detection by the background subtraction. Finally, the mean shift algorithm is utilized to track vehicles. The secondary selected strategy is a new algorithm designed in this article .It can reconstruct quickly the accurate background from the crowd video frames. Using background subtraction can eliminate the interference of background on the color probability density of target in mean shift algorithm. The whole algorithm achieves the real-time tracking in complicated situation in a high accuracy.
Mean-shift
Tracking (education)
Subtraction
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Background subtraction is an essential step of intelligent video surveillance and has got a lot of interest among researcher community in recent years. It has a critical impact on the performance of object tracking and activity analysis. In this paper we propose a new multi-level background modeling to overcome dynamic background problem. At rst, an image is segmented to foreground or background in larger window(12 12) level and results are rened by smaller windows(6 6 and3 3)level and pixel based operation. Our pixel based segmentation section uses VIBE method , which is fast and needs less memory to conserve background model components, with some modication to detect shadows. Subtraction is done in coarse levels rstly, and resulted foreground are investigated more by smaller windows(ne levels). This makes the algorithm to be more ecient. A new once-o background changing detection and model updating is proposed to make our algorithm as accurate as possible. The last part of our algorithm is enhancement where, we have used morphological operators in order to improve the subtraction quality. The approach provides us with many advantages compared to the state-of-the-art. Experimental results clearly justify our strategy.
Foreground detection
Background image
Tracking (education)
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The paper proposes a dynamic background upgrading method based on frame difference and the secondary background subtraction.For the first step,frames subtraction and initial background subtraction is applied to get the effective moving object region.And then computer the difference between the current image and the current background image in order to get the moving target.Finally,after a comparison,the background region,which is used to construct and upgrade the background model latterly,and foreground region was separated.This method can solve the problem that the background upgrading is hard to be completed quickly when the moving object comes to still suddenly.It can effectively take stopped motion object into background in the processing of background upgrading;it also simplifies the background model and improves the sensitivity and real time operation of moving object detecting.
Background image
Upgrade
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