Bayesian Tracking with Auxiliary Discrete Processes. Application to Detection and Tracking of Objects with Occlusions

Pérez, Patrick; Vermaak, J.

doi:10.1007/978-3-540-70932-9_15

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…In general, the selection of an adequate state space is a compromise between two goals: on one hand, the state space should provide the richest information to further higher level analysis modules, and be precise enough so as to model as well as possible the information in the image and video. In other words (and even if such information is not requested by the application), adding relevant auxiliary variables in the state space that simplifies the modeling of other components (dynamics, appearance) is often useful [Perez and Vermaak, 2005]. On the other hand, the state has to remain simple enough and appropriate to the quality level of the data (i.e.…”

Section: Monte-carlo Methodsmentioning

confidence: 99%

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: Monte-carlo Methodsmentioning

confidence: 99%

Sampling techniques for audio-visual tracking and head pose estimation

Odobez¹,

Lanz²

2012

Multimodal Signal Processing

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“…For a given plot, only two components are displayed have been used to track objects when different types of dynamics can occur (Isard and Blake 1998). Other examples of auxiliary discrete variables beside the main hidden state of interest are given in (Perez and Vermaak 2005). Since τ a(t) and γ t are highly correlated their simultaneous estimation will give results that are more robust and accurate than results obtained with methods estimating them in sequence.…”

Section: Approachmentioning

confidence: 98%

Simultaneous Facial Action Tracking and Expression Recognition in the Presence of Head Motion

Dornaika

Davoine

2007

Int J Comput Vis

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The recognition of facial gestures and expressions in image sequences is an important and challenging problem. Most of the existing methods adopt the following paradigm. First, facial actions/features are retrieved from the images, then the facial expression is recognized based on the retrieved temporal parameters. In contrast to this mainstream approach, this paper introduces a new approach allowing the simultaneous retrieval of facial actions and expression using a particle filter adopting multi-class dynamics that are conditioned on the expression. For each frame in the video sequence, our approach is split into two consecutive stages. In the first stage, the 3D head pose is retrieved using a deterministic registration technique based on Online Appearance Models. In the second stage, the facial actions as well as the facial expression are simultaneously retrieved using a stochastic framework based on second-order Markov chains. The proposed fast scheme is either as robust as, or more robust than existing ones in a number of respects. We describe extensive experiments and provide evaluations of performance to show the feasibility and robustness of the proposed approach.

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“…Such models have been used to track objects when different types of dynamics can occur (Isard & Blake, 1998). Other examples of auxiliary discrete variables beside the main hidden state of interest are given in (Perez & Vermaak, 2005). Since τ a(t) and γ t are highly correlated their simultaneous estimation will give results that are more robust and accurate than results obtained with methods estimating them in sequence.…”

Section: Approachmentioning

confidence: 99%