Probabilistic Color and Adaptive Multi-Feature Tracking with Dynamically Switched Priority Between Cues

Badrinarayanan, Vijay; Pérez, Patrick; Clerc, François; Oisel, Lionel

doi:10.1109/iccv.2007.4408955

Cited by 51 publications

(46 citation statements)

References 10 publications

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“…The performance depends strongly on how well the reference model matches the target's appearance at its current state. To handle pose changes and uneven illumination, either additional cues invariant to those factors such as motion, depth, and sound are used , Badrinarayanan et al, 2007, or the external part of the object (the local background) is included in the likelihood definition [Lehuger et al, 2006]. To avoid drift, i.e.…”

Section: Person Tracking In Roomsmentioning

confidence: 99%

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Sampling techniques for audio-visual tracking and head pose estimation

Odobez¹,

Lanz²

2012

Multimodal Signal Processing

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Analyzing people behaviors in smart environment using multimodal sensors requires to answer a set of typical questions: who are the people, where are they, what activities are they doing, when, with whom are they interacting, and how. In this view, locating people or their faces and characterizing them (e.g. extracting their body or head orientation) allows to address the first two questions (who and where), and is usually one of the first steps before applying higher level multimodal scene analysis algorithms that address the other questions. In the last ten years, tracking algorithms have experienced considerable progresses, particularly in indoor environment or for specific applications, where they have reached a maturity allowing their deployment in real systems and applications. Nevertheless, there are still several issues that can make the tracking difficult: background clutter, potentially small object size; complex shape, appearance, and motion, and their changes over time or across camera views; inaccurate/rough scene calibration or inconsistent camera calibration between views for 3D tracking; real-time processing requirements. In what follows, we discuss some important aspects of tracking algorithms, and ultimately introduce the remaining of the chapter content.Scenarios and Set-ups. Scenarios and application needs strongly influence the considered physical environment, and therefore the set-up (where, how many, and what type of sensors are used) and choice of a tracking 84

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Section: Person Tracking In Roomsmentioning

confidence: 99%

“…To avoid drift, i.e. adaptation to background clutter and subsequent locking, the weights associated to the fusion scheme must be carefully selected and, ideally, updated on-line from data evidence [Badrinarayanan et al, 2007].…”

Section: Person Tracking In Roomsmentioning

confidence: 99%

Sampling techniques for audio-visual tracking and head pose estimation

Odobez¹,

Lanz²

2012

Multimodal Signal Processing

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“…Related tracking methods can be found in the extensive literature on multi-cue tracking [3,13,20]. The benefit of these approaches is increased robustness when different cues have different failure modes and therefore complement each other.…”

Section: Hand Trackingmentioning

confidence: 99%

“…The benefit of these approaches is increased robustness when different cues have different failure modes and therefore complement each other. The most common idea is to run several trackers in parallel and subsequently combine their output, by either selecting between them [3] or by probabilistically merging them [13,20]. In contrast, the proposed tracker switches between trackers (and corresponding features) entirely, therefore not requiring trackers to run simultaneously.…”

Section: Hand Trackingmentioning

confidence: 99%

AIDIA - Adaptive Interface for Display InterAction

Stenger

Woodley²,

Kim

et al. 2008

Procedings of the British Machine Vision Conference 2008

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This paper presents a vision-based system for interaction with a display via hand pointing. An attention mechanism based on face and hand detection allows users in the camera's field of view to take control of the interface. Face recognition is used for identification and customisation. The system allows the user to control the screen pointer by tracking their fist. On-screen items can be selected using one of four activation mechanisms. Current sample applications include browsing image and video collections as well as viewing a gallery of 3D objects. In experiments we demonstrate the performance of the vision components in challenging conditions and compare it to that of other systems.

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“…Several reliability measures have been proposed in the past. For example, in [2,13], the uncertainty of each single-cue tracker is measured in terms of the spatial spread of the associated particles. Alternatively, in [17] a score is assigned to each feature which quantifies the difference between the tracking result obtained by the cue alone and the result obtained using all the cues.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Partitioned Sampling For Tracking With Discriminative Features

Duffner

Odobez

Ricci

2009

Procedings of the British Machine Vision Conference 2009

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We present a multi-cue fusion method for tracking with particle filters which relies on a novel hierarchical sampling strategy. Similarly to previous works, it tackles the problem of tracking in a relatively high-dimensional state space by dividing such a space into partitions, each one corresponding to a single cue, and sampling from them in a hierarchical manner. However, unlike other approaches, the order of partitions is not fixed a priori but changes dynamically depending on the reliability of each cue, i.e. more reliable cues are sampled first. We call this approach Dynamic Partitioned Sampling (DPS). The reliability of each cue is measured in terms of its ability to discriminate the object with respect to the background, where the background is not described by a fixed model or by random patches but is represented by a set of informative "background particles" which are tracked in order to be as similar as possible to the object. The effectiveness of this general framework is demonstrated on the specific problem of head tracking with three different cues: colour, edge and contours. Experimental results prove the robustness of our algorithm in several challenging video sequences.

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Probabilistic Color and Adaptive Multi-Feature Tracking with Dynamically Switched Priority Between Cues

Cited by 51 publications

References 10 publications

Sampling techniques for audio-visual tracking and head pose estimation

Sampling techniques for audio-visual tracking and head pose estimation

AIDIA - Adaptive Interface for Display InterAction

Dynamic Partitioned Sampling For Tracking With Discriminative Features

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