Segmentation, tracking and interpretation using panoramic video

Nicolescu, Mircea; Medioni, Gérard; Lee, Mi-Suen

doi:10.1109/omnvis.2000.853826

Cited by 14 publications

(11 citation statements)

References 7 publications

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“…In the simplest case, background pixels are modeled using a single video-frame, or they can be represented using Gaussian [31] or non-parametric distributions [13]. Models can be adaptive to slowly changing conditions [13], [24], [34], and even robust to smooth and linear camera movements [17]. In all these cases, pixels in subsequent frames are compared to the background model.…”

Section: A Related Work In Visual Identification / Trackingmentioning

confidence: 99%

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A flexible control architecture for extended autonomy of robotic assistants

King¹,

Palathingal²,

Nicolescu³

2009

jopha

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Abstract-A major challenge in deploying robots into the real world is the design of an architectural framework which can provide extended, natural and effective interactions with people. Within this framework, key issues that need to be solved relate to the robots' ability to engage in interactions in a natural way, to deal with multiple users, and to be continually perceptive of their surroundings. In this paper we propose a robot control architecture that addresses these issues. Our architecture has three main key features. First, it enables the representation of complex, sequential and hierarchical robot tasks, typically needed for service applications, in a behavior-based framework. Second, it provides the robot with flexibility in dealing with multiple users, such as to accommodate multiple user requests and task interruptions, over extended periods. Third, through its visual detection mechanism, the architecture allows the robot to identify when people are requesting its interaction. We demonstrate our approach on a Pioneer 3DX mobile robot, performing service tasks in a real-world environment.Index Terms-Human-robot interaction, behavioral robotics, personal robots.

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Section: A Related Work In Visual Identification / Trackingmentioning

confidence: 99%

“…Segmentation is accomplished using an adaptive background modeling technique similar to the one described in [24]. Each pixel of the background is modeled as a Gaussian distribution in the RGB color space, with the mean (µ r ,µ g ,µ b ) and standard deviation (σ r ,σ g ,σ b ).…”

Section: Trainingmentioning

confidence: 99%

A flexible control architecture for extended autonomy of robotic assistants

King¹,

Palathingal²,

Nicolescu³

2009

jopha

View full text Add to dashboard Cite

show abstract

“…15,16 Therefore, our objective is to use the Mixture-of-Gaussians scene model for active cameras by means of a panoramic representation of the scene.…”

Section: Detectionmentioning

confidence: 99%

Importance of detection for video surveillance applications

et al. 2008

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Abstract. Though it is the first step of a real video surveillance application, detection has received less attention than tracking in research on video surveillance. We show, however, that the majority of errors in the tracking task are due to wrong detection. We show this by experimenting with a multi object tracking algorithm based on a Bayesian framework and a particle filter. This algorithm, which we have named iTrack, is specifically designed to work in practical applications by defining a statistical model of the object appearance to build a robust likelihood function. Likewise, we present an extension of a background subtraction algorithm to deal with active cameras. This algorithm is used in the detection task to initialize the tracker by means of a prior density. By defining appropriate performance metrics, the overall system is evaluated to elucidate the importance of detection for video surveillance applications.

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“…Dellaert et al proposed a fast image registration algorithm between the image from a pan/tilt camera and background images from a database [3]. Onmi-directional cameras were also used to extend the field of view to 360˚ [4]. But the reality remains that most tracking algorithms cater only to the case of fixed cameras and are generally based on adaptive background generation and subtraction [2,5,6].…”

mentioning

confidence: 99%