Real time Background Subtraction techniques for detection of moving objects in video surveillance system

Komagal, E.; Vinodhini, Arthy; Archana, .; Bricilla,

doi:10.1109/iccca.2012.6179191

Cited by 18 publications

(7 citation statements)

References 5 publications

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“…The proposed method uses the background subtraction method (9) to estimate feature value. The method extracts the object that did not exist in the previous frame by comparing the current image and the previous image.…”

Section: Feature Acquisition Methodsmentioning

confidence: 99%

Simultaneous Estimation of Horizontal Position and Depth Distance by Kalman Filter and Obrid-Sensor

Horikawa

Hamasuna

et al. 2019

Proceedings of the 7th International Conference on Intelligent Systems and Image Processing 2019

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Recently, the demand of nursing care rapidly has been increasing along with the progress of serious aging society in Japan (1) . Accordingly, the physical burden to the caregivers is increasing. In care works, it is important to watch over care-receivers with privacy preservation in the house or the nursing home. Thus, it is difficult to use the systems with security or surveillance cameras that might violate the care-receivers' privacy. In order to solve the problems, the Obrid-Sensor that can detect the position and condition of the subject without privacy violation has been developed in previous researches. However, the Obrid-Sensor did not correspond the detection of multiple subjects. Moreover, in order to obtain the subject's position information from a higher perspective, a complex installation of the Obrid-Sensor has to be introduced. Therefore, we propose the tracking method by estimating the horizontal and depth position of the subject simultaneously with one Obrid-Sensor in this research. The installation of the Obrid-Sensor in the proposed method is simple. It is also possible to track multiple subjects respectively at the same time.

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Section: Feature Acquisition Methodsmentioning

confidence: 99%

Simultaneous Estimation of Horizontal Position and Depth Distance by Kalman Filter and Obrid-Sensor

Horikawa

Hamasuna

et al. 2019

Proceedings of the 7th International Conference on Intelligent Systems and Image Processing 2019

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“…In order to detect the subject, the proposed system uses the background subtraction method. The background subtraction method is the image processing method to extract the object that did not exist in the previous image by comparing both the current image and the previous image (9) . In this paper, the Obrid-Sensor is installed with the line sensor horizontal and obtain the brightness distribution waveform of the subject by applying this extraction method.…”

Section: Subject Detection Methodsmentioning

confidence: 99%

Proposal of Leaving the Bed Motion Detection Method Corresponded at Night using Obrid-Sensor

Hamasuna

Nakashima

2019

Proceedings of the 7th IIAE International Conference on Industrial Application Engineering 2019

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Recently, due to the aging of population (1) , the increasing of dementia patients has become social problem. The dementia patients are feared to go wander, and some of them may be gone missing after going out and have a risk to encounter accident (2) . However, watching over them all the time is difficult. Especially watching over at midnight or after sleeping is mental burden for helpers. In order to solve the problem, watching by camera is given, but it is difficult to use cameras in daily life from the viewpoint of privacy violation.As the solution for this problem, we have developed one-dimensional brightness distribution sensor (Obrid-Sensor) that can detect the subject's position, motion and condition without violating their privacy. In this paper, the method of leaving the bed detection that detect the leaving the bed motion day and night by installing the sensor in the edge of in the bed and irradiating infrared light is proposed.

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“…To perform high level analysis tasks, these tools include moving object detection strategies [2]. Among these strategies, nonparametric modeling methods have shown to be those providing the best quality detections in a very large variety of scenarios [3].…”

Section: Introductionmentioning

confidence: 99%

GPGPU implementation of an improved nonparametric background modeling for moving object detection strategies

Cuevas¹,

Berjón²,

Morán³

et al. 2013

2013 IEEE International Conference on Consumer Electronics (ICCE)

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Real time Background Subtraction techniques for detection of moving objects in video surveillance system

Cited by 18 publications

References 5 publications

Simultaneous Estimation of Horizontal Position and Depth Distance by Kalman Filter and Obrid-Sensor

Simultaneous Estimation of Horizontal Position and Depth Distance by Kalman Filter and Obrid-Sensor

Proposal of Leaving the Bed Motion Detection Method Corresponded at Night using Obrid-Sensor

GPGPU implementation of an improved nonparametric background modeling for moving object detection strategies

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