Publications (4)0 Total impact
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Conference Proceeding: On the use of perspective catadioptric sensors for 3D model-based tracking with particle filters
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ABSTRACT: We present a model-based 3D tracking system, using wide angle perspective catadioptric sensors. These sensors acquire 360deg views of the environment and the projection from 3D world points to the image plane is approximated by a perspective model. This is a major advantage in structured environments because straight lines on specific surfaces are not deformed by the sensor, allowing the application of standard computer vision algorithms. Objects off the surface are distorted according to a complex projection model, but can be approximated by a simple wide angle perspective mapping. This is exploited here to develop a robust tracking system for autonomous robots using a 3D shape and color-based object model. The use of particle filters allows tracking to be done with 3D realistic motion models and tackling object occlusion, overlap and ambiguities. We show that the use of the perspective model is advantageous over more standard catadioptric projection models, since it renders a very good approximation to the true model, being simpler and more efficient to use, in particular with 3D particle filtering methods.Intelligent Robots and Systems, 2007. IROS 2007. IEEE/RSJ International Conference on; 12/2007 -
Article: Sample-based 3D tracking of colored objects: A flexible architecture
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ABSTRACT: This paper presents a method for 3D model-based tracking of colored ob-jects using a sampling methodology. The problem is formulated in a Monte Carlo filtering approach, whereby the state of an object is represented by a set of hypotheses. The main originality of this work is an observation model consisting in the comparison of the color information in some sam-pling points around the target's hypothetical edges. On the contrary to ex-isting approaches the method does not need to explicitly compute edges in the video stream, thus dealing well with optical or motion blur. The method does not require the projection of the full 3D object on the image, but just of some selected points around the target's boundaries. This allows a flexible and modular architecture illustrated by experiments performed with different objects (balls and boxes), camera models (perspective, catadioptric, dioptric) and tracking methodologies (particle and Kalman filtering). -
Article: Color 3D model-based tracking with arbitrary projection models
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ABSTRACT: We present a color-and shape-based 3D tracking system suited to a large class of vision sensors. The method is, in fact, applicable with any projec-tion model, provided that it is calibrated and the projection function is known. The tracking architecture is based on Particle Filtering methods where each parti-cle represents the 3D state of the object, rather that its state in the image, therefore bypassing the nonlinearity caused by the projection model. This allows the use of realistic 3D motion models and easy integration of the sensor self-motion mea-surements. All nonlinearities are concentrated in the observation model that, for each particle, projects a few tens of special points onto the image, on (and around) the 3D object's surface. The likelihood of each state is then evaluated using color histograms. Since only pixel access operations are required, the method does not involve costly image processing routines like edge/feature extraction, color seg-mentation or 3D reconstruction, that can be cumbersome with omnidirectional projection models. The tracking system copes well with motion and optical blur. We show applications of tracking various objects (balls, boxes) in mobile robots with catadioptric and dioptric omnidirectional sensors. -
Article: Tracking objects with generic calibrated sensors: An algorithm based on color and 3D shape features
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ABSTRACT: We present a color and shape based 3D tracking system suited to a large class of vision sensors. The method is applicable, in principle, to any known calibrated projection model. The tracking architecture is based on particle filtering methods where each particle represents the 3D state of the object, rather than its state in the image, therefore overcoming the nonlinearity caused by the projection model. This allows the use of realistic 3D motion models and easy incorporation of self-motion measurements. All nonlinearities are concentrated in the observation model so that each particle projects a few tens of special points onto the image, on (and around) the 3D object’s surface. The likelihood of each state is then evaluated by comparing the color distributions inside and outside the object’s occluding contour. Since only pixel access operations are required, the method does not require the use of image processing routines like edge/feature extraction, color segmentation or 3D reconstruction, which can be sensitive to motion blur and optical distortions typical in applications of omnidirectional sensors to robotics. We show tracking applications considering different objects (balls, boxes), several projection models (catadioptric, dioptric, perspective) and several challenging scenarios (clutter, occlusion, illumination changes, motion and optical blur). We compare our methodology against a state-of-the-art alternative, both in realistic tracking sequences and with ground truth generated data.Robotics and Autonomous Systems.
