3D real-time head tracking fusing color histograms and stereovision

Moreno, Francesc; Tarrida, A.; Andrade‐Cetto, Juan; Sanfeliu, Alberto

doi:10.1109/icpr.2002.1044727

Cited by 23 publications

(22 citation statements)

References 8 publications

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“…3. The main steps of the developed robust 3D face tracker loss of generality, the 3D face motion will be expressed with respect to the head coordinate system associated with the first video frame 1 . In order to invoke the registration algorithm one has to compute the 3D coordinates of the vertices associated with the two different video frames: the initial video frame and the current one (see Figure 4).…”

Section: Tracking By Aligning Texture Maps and Stereo-based 3d Modelsmentioning

confidence: 99%

“…These are either based on magnetic sensors or on special markers placed on the face; both practices are encumbering, causing discomfort and limiting natural motion. Vision-based 3D head tracking provides an attractive alternative since vision sensors are not invasive and hence natural motions can be achieved [1]. However, detecting and tracking faces in video sequences is a challenging task due to the image variability caused by pose, expression, and illumination changes.…”

Section: Introductionmentioning

confidence: 99%

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Rigid and Non-rigid Face Motion Tracking by Aligning Texture Maps and Stereo-Based 3D Models

Dornaika

Sappa

2006

Advanced Concepts for Intelligent Vision Systems

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Abstract. Accurate rigid and non-rigid tracking of faces is a challenging task in computer vision. Recently, appearance-based 3D face tracking methods have been proposed. These methods can successfully tackle the image variability and drift problems. However, they may fail to provide accurate out-of-plane face motions since they are not very sensitive to out-of-plane motion variations. In this paper, we present a framework for fast and accurate 3D face and facial action tracking. Our proposed framework retains the strengths of both appearance and 3D data-based trackers. We combine an adaptive appearance model with an online stereo-based 3D model. We provide experiments and performance evaluation which show the feasibility and usefulness of the proposed approach.

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Section: Tracking By Aligning Texture Maps and Stereo-based 3d Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rigid and Non-rigid Face Motion Tracking by Aligning Texture Maps and Stereo-Based 3D Models

Dornaika

Sappa

2006

Advanced Concepts for Intelligent Vision Systems

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“…Later on, we came up with a simpler temporal quality model and presented it in Baden-Baden on August 2001 [9]. On the other hand, our study of estimation theory gave birth to a technical report on Kalman filtering [6], and to a pair of contributions on its use in a related application, the tracking of human faces by our mobile robot [145,146].…”

Section: Dynamic Environmentsmentioning

confidence: 99%

Environment Learning for Indoor Mobile Robots

Andrade‐Cetto¹

2006

Springer Tracts in Advanced Robotics

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This thesis focuses on the various aspects of autonomous environment learning for indoor service robots. Particularly, on landmark extraction from sensor data, autonomous map building, and robot localization.To univocally identify landmarks from sensor data, we study several landmark representations, and the mathematical foundation necessary to extract the features that build them from images and laser range data. The features extracted from just one sensor may not suffice in the invariant characterization of landmarks and objects, pushing for the combination of information from multiple sources. We present a new algorithm that fuses complementary information from two low level vision modules into coherent object models that can be tracked and learned in a mobile robotics context. Illumination conditions and occlusions are the most prominent artifacts that hinder data association in computer vision. By using photogrammetric and geometric constraints we restrict the search for landmark matches in successive images, and by locking our interest in one or a set of landmarks in the scene, we track those landmarks along successive frames, reducing considerably the data association problem. We concentrate on those tools from the geometry of multiple views that are relevant to the computation of initial landmark location estimates for coarse motion recovery; a desirable characteristic when odometry is not available or is highly unreliable.Once landmarks are accurately extracted and identified, the second part of the problem is to use these observations for the localization of the robot, as well as the refinement of the landmark location estimates. We consider robot motion and sensor observations as stochastic processes, and treat the problem from an estimation theoretic point of view, dealing with noise by using probabilistic methods.The main drawback we encounter is that current estimation techniques have been devised for static environments, and that they lack robustness in more realistic situations. To aid in those situations in which landmark observations might not be consistent in time, we propose a new set of temporal landmark quality functions, and show how iii by incorporating these functions in the data association tests, the overall estimationtheoretic approach to map building and localization is improved. The basic idea consists on using the history of data association mismatches for the computation of the likelihood of future data association, together with the spatial compatibility tests already available.Special attention is paid in that the removal of spurious landmarks from the map does not violate the basic convergence properties of the localization and map building algorithms already described in the literature; namely, asymptotic convergence and full correlation.The thesis also gives an in depth analysis of the fully correlated model to localization and map building from a control systems theory point of view. Considering the fact that the Kalman filter is nothing else but an optimal observer,...

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“…One of our first approaches combines information of colour changes and depth for face tracking in real time [4]. The purpose is to follow a face or an object that has colour and depth continuity avoiding the loss of them due to the presence of similar colour in the background.…”

Section: Developing "Robust Techniques" For Object and Face Trackingmentioning

confidence: 99%

Robot Vision for Autonomous Object Learning and Tracking

Sanfeliu

2003

Lecture Notes in Computer Science

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Abstract. In this paper we present a summary of some of the research that we are developing in the Institute of Robotics of the CSIC-UPC, in the field of Learning and Robot Vision for autonomous mobile robots. We describe the problems that we have found and some solutions that have been applied in two issues: tracking objects and learning and recognition of 3D objects in robotic environments. We will explain some of the results accomplished.

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3D real-time head tracking fusing color histograms and stereovision

Abstract: Abstract

Cited by 23 publications

References 8 publications

Rigid and Non-rigid Face Motion Tracking by Aligning Texture Maps and Stereo-Based 3D Models

Rigid and Non-rigid Face Motion Tracking by Aligning Texture Maps and Stereo-Based 3D Models

Environment Learning for Indoor Mobile Robots

Robot Vision for Autonomous Object Learning and Tracking

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