A real-time system for monitoring of cyclists and pedestrians

Heikkilä, Janne; Silvén, Olli

doi:10.1016/j.imavis.2003.09.010

Cited by 93 publications

(72 citation statements)

References 8 publications

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“…One easy approach for modelling the background is through a single color/grayscale image of moving objects in the scene which acquired without motion or estimated via a temporal median filter (Cucchiara et al, 2005;Heikkilä and Silvén, 2004;Zhou and Aggarwal;2001 (Calderara et al, 2006;Cucchiara et al, 2005). These methods can be recursive or non-recursive approaches.…”

Section: Methodsmentioning

confidence: 99%

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Background subtraction methods in video streams: A review

Joudaki

Sunar

Kolivand

2015

2015 4th International Conference on Interactive Digital Media (ICIDM)

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Background subtraction is one of the most important parts in image and video processing field. There are some unnecessary parts during the image or video processing, and should be removed, because they lead to more execution time or required memory. Several subtraction methods have been presented for the time being, but find the best-suited method is an issue, which this study is going to address. Furthermore, each process needs to the specific subtraction technique, and knowing this issue helps researchers to achieve faster and higher performance in their research. This paper presents a comparative study of several existing background subtraction methods which have been investigated from simple background subtraction to more complex statistical techniques. The goal of this study is to provide a view of the strengths and drawbacks of the widely used methods. The methods are compared based on their memory requirement, the computational time and their robustness of different videos. Finally, a comparison between the existing methods has been employed with some factors like computational time or memory requirements. It is also hoped that this analysis helps researchers to address the difficulty of selecting the most convenient method for background subtraction.

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Section: Methodsmentioning

confidence: 99%

“…The method assumes the object as temporary in time. Background model will affect significantly in a slow or large target (Heikkilä and Silvén, 2004;Zhou and Aggarwal, 2001). Some studies also have been done with the background as the average of the previous n frames (François and Medioni, 1999).…”

Section: Average Filteringmentioning

confidence: 99%

Background subtraction methods in video streams: A review

Joudaki

Sunar

Kolivand

2015

2015 4th International Conference on Interactive Digital Media (ICIDM)

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“…A background subtraction method 20 segments the picture and compares intensity and color with a periodically updated reference background image not containing the moving object 21 . 3D information is obtained by using a calibration step which computes the transformation of a 2D image referential point to a 3D scene referential point.…”

Section: Object Detection and Trackingmentioning

confidence: 99%

A computer system to monitor older adults at home: Preliminary results

Zouba¹,

Brémond²,

Thonnat³

et al. 2009

Gerontechnology

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Mailland, O. Guerin, A computer system to monitor older adults at home: Preliminary results. Gerontechnology 2009;8(3):129-139, doi: 10.4017/gt.2009.08.03.011.00 Determining the individual transition from the 3 rd to the 4 th or frailty phase of life is important for both the safety of the older person and to support the care provider. We developed an automatic monitoring system consisting of cameras and different sensors that analyze human behaviors and looks for changes in activities by detecting the presence of people, their movements, and automatically recognizing events and Activities of Daily Living (ADLs). Assessment took place in a laboratory environment (GERHOME) comprised of four rooms (kitchen, living-room, bedroom, and bathroom). Data from 2 volunteers (64 and 85 years old) were analyzed. Precision in recognizing postures and events ranged from 62-94%, while sensitivity fell in the range of 62-87%. The system could differentiate ADL levels for the 64 and 85 year old subjects. These results are promising and merit replication and extension. Considerable work remains before the complete transition from 3 rd to 4 th life phase can be reliably detected. The GERHOME system is promising in this respect

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“…It needs to keep track of the pixel value history. To avoid this problem a first order recursive filter is used to update the background model [5]. It easily causes 'tailing' or 'ghosting'.…”

Section: Related Workmentioning

confidence: 99%