From tracking to advanced surveillance

Fuentes, Luis M.; Velastín, Sergio A.

doi:10.1109/icip.2003.1247196

Cited by 16 publications

(11 citation statements)

References 4 publications

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“…For example, the threshold should be smaller for regions with low contrast. One possible modification is proposed by Fuentes and Velastin [29]. * Stauffer and Grimson use α · η(It; µî ,t , σî ,t ) to compute ρ in [6], which is incorrect as pointed out in [26].…”

Section: Foreground Detectionmentioning

confidence: 99%

Robust techniques for background subtraction in urban traffic video

2004

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Identifying moving objects from a video sequence is a fundamental and critical task in many computer-vision applications. A common approach is to perform background subtraction, which identifies moving objects from the portion of a video frame that differs significantly from a background model. There are many challenges in developing a good background subtraction algorithm. First, it must be robust against changes in illumination. Second, it should avoid detecting non-stationary background objects such as swinging leaves, rain, snow, and shadow cast by moving objects. Finally, its internal background model should react quickly to changes in background such as starting and stopping of vehicles. In this paper, we compare various background subtraction algorithms for detecting moving vehicles and pedestrians in urban traffic video sequences. We consider approaches varying from simple techniques such as frame differencing and adaptive median filtering, to more sophisticated probabilistic modeling techniques. While complicated techniques often produce superior performance, our experiments show that simple techniques such as adaptive median filtering can produce good results with much lower computational complexity.

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Section: Foreground Detectionmentioning

confidence: 99%

Robust techniques for background subtraction in urban traffic video

2004

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“…(d) Use, as far as possible, of current site infrastructure (hardware and people) to improve the chances of early deployment with evolutionary changes in organisational procedures and equipment. The distributed surveillance system being developed as part of PRISMATICA and the associated user interface are described in more detail in Velastin et al (2002aVelastin et al ( ,b), (2003 and Fuentes and Velastin (2003). In this paper, we concentrate on aspects (b) and (c) above, i.e.…”

Section: Control Roomsmentioning

confidence: 99%

A flexible communications protocol for a distributed surveillance system

Velastín¹,

Lo²,

Sun³

2004

Journal of Network and Computer Applications

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“…Finally, there has been a vast amount of research on using automated closed circuit television systems (CCTVs) to monitor people. This includes work on monitoring and predicting crowd flow [5], tracking individual movements of people [11,12], and a variety of other computer vision based tracking research [16,17].…”

Section: Introductionmentioning

confidence: 99%

A cooperative control algorithm for camera based observational systems.

Young¹

2012

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Over the last several years, there has been considerable growth in camera based observation systems for a variety of safety, scientific, and recreational applications. In order to improve the effectiveness of these systems, we frequently desire the ability to increase the number of observed objects, but solving this problem is not as simple as adding more cameras. Quite often, there are economic or physical restrictions that prevent us from adding additional cameras to the system. As a result, we require methods that coordinate the tracking of objects between multiple cameras in an optimal way.In order to accomplish this goal, we present a new cooperative control algorithm for a camera based observational system. Specifically, we present a receding horizon control where we model the underlying optimal control problem as a mixed integer linear program. The benefit of this design is that we can coordinate the actions between each camera while simultaneously respecting its kinematics. In addition, we further improve the quality of our solution by coupling our algorithm with a Kalman filter. Through this integration, we not only add a predictive component to our control, but we use the uncertainty estimates provided by the filter to encourage the system to periodically observe any outliers in the observed area. This combined approach allows us to intelligently observe the entire region of interest in an effective and thorough manner.3 4

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From tracking to advanced surveillance

Cited by 16 publications

References 4 publications

Robust techniques for background subtraction in urban traffic video

Robust techniques for background subtraction in urban traffic video

A flexible communications protocol for a distributed surveillance system

A cooperative control algorithm for camera based observational systems.

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