Background modelling and background subtraction performance for object detection

Mohamed, Shahrizat Shaik; Tahir, Nooritawati Md; Adnan, Ramli

doi:10.1109/cspa.2010.5545291

Cited by 59 publications

(18 citation statements)

References 5 publications

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“…Frame subtraction method extract the target by temporal difference and thresholding between two or three consecutive frames of the video sequence, it is easy to implement and has strong adaptability to the dynamic environment, but it is sensitive to illumination variant and will bring forth cavitation. Background subtraction get the object target via the difference of current frame and background frame, it can better adapt to the changes of illumination but cannot deal with the problem of scene light rapid change [15][16][17][18][19]. So in this paper, we combine Gaussian mixture modeling which is an effective method of background subtraction [20] with three-frame-differencing to extract the foreground object.…”

Section: Pedestrian Detection Via Segmentation and Ellipse Fittingmentioning

confidence: 95%

Pedestrian Detection and Counting Based on Ellipse Fitting and Object Motion Continuity for Video Data Analysis

Wang

Zhang

2015

Intelligent Computing Theories and Methodologies

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Abstract. In order to detect and count pedestrians in different kinds of scenes, this paper put forward a method of solving the problem on video sequences captured from a fixed camera. After preprocessing operations on the original video sequences (Gaussian mixture modeling, three-frame-differencing, image binaryzation, Gaussian filtering, dilation and erosion) we extract the relatively complete pedestrian contours. Then we use the least square ellipse fitting method on those contours that has been extracted, the center of the ellipse is undoubtedly regarded as the tracking point of a pedestrian. With those points, a pedestrian matching pursuit and counting algorithm based on object motion continuity is used for tracking and counting pedestrians, this method can be better used in those scenes which are sparse and rarely obscured. Experiments validate that our pedestrian matching pursuit and counting algorithm has obvious superiorities: good real-time performance and high accuracy.

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Section: Pedestrian Detection Via Segmentation and Ellipse Fittingmentioning

confidence: 95%

Pedestrian Detection and Counting Based on Ellipse Fitting and Object Motion Continuity for Video Data Analysis

Wang

Zhang

2015

Intelligent Computing Theories and Methodologies

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“…3. 2 First, we take a frame from the video sequence as the background image in which nobody is in the scene. The background subtraction is applied to frames with human activities in order to extract foreground regions.…”

Section: A Scheme Overviewmentioning

confidence: 99%

“…Then the background was updated according to the factor. Mohamed et al used mixture of Gaussian distribution (MoG) background subtraction method to detect moving objects [2]. This was done by proper selection of parameters of MoG.…”

Section: Introductionmentioning

confidence: 99%

Segmentation by temporal detection integration

Wang

Lee

2011

2011 18th IEEE International Conference on Image Processing

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We propose a novel method, called segmentation by temporal detection integration (STDI), to improve the segmentation results of background subtraction. The STDI applies split and merge algorithms forwardly and backwardly to obtain appropriate region segmentations based on the integration of the temporal detections across frames. The proposed scheme can be applied to human detection and tracking to avoid the situations that a group of people being detected as one single segment or a human body being separated into fragments, thus making the subsequent human tracking more reliable. To verify the performance of the proposed STDI, a dynamic layer model is used to depict motion, appearance, and shape of detections for human tracking. Experimental results show that the proposed STDI significantly improves the background subtraction segmentation results, and therefore, the accuracy of human tracking.

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“…There are some traditionally and widely used algorithms including frame difference method, traditional background subtraction method, optical flow method and statistical learning method [16]. The previous two methods only work on a stationary background.…”

Section: Introductionmentioning

confidence: 99%

Moving Object Detection Using Scanning Camera on a High-Precision Intelligent Holder

Chen

et al. 2016

Sensors

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During the process of moving object detection in an intelligent visual surveillance system, a scenario with complex background is sure to appear. The traditional methods, such as “frame difference” and “optical flow”, may not able to deal with the problem very well. In such scenarios, we use a modified algorithm to do the background modeling work. In this paper, we use edge detection to get an edge difference image just to enhance the ability of resistance illumination variation. Then we use a “multi-block temporal-analyzing LBP (Local Binary Pattern)” algorithm to do the segmentation. In the end, a connected component is used to locate the object. We also produce a hardware platform, the core of which consists of the DSP (Digital Signal Processor) and FPGA (Field Programmable Gate Array) platforms and the high-precision intelligent holder.

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Background modelling and background subtraction performance for object detection

Cited by 59 publications

References 5 publications

Pedestrian Detection and Counting Based on Ellipse Fitting and Object Motion Continuity for Video Data Analysis

Pedestrian Detection and Counting Based on Ellipse Fitting and Object Motion Continuity for Video Data Analysis

Segmentation by temporal detection integration

Moving Object Detection Using Scanning Camera on a High-Precision Intelligent Holder

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