Antonio S. Micilotta

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This paper presents a probabilistic framework of assembling detected human body parts into a full 2D human configuration. The face, torso, legs and hands are detected in cluttered scenes using boosted body part detectors trained by AdaBoost. Body configurations are assembled from the detected parts using RANSAC, and a coarse heuristic is applied to(More)
This paper presents a real time approach to locate and track the upper torso of the human body. Our main interest is not in 3D biometric accuracy, but rather a sufficient discriminatory representation for visual interaction. The algorithm employs background suppression and a general approximation to body shape, applied within a particle filter framework,(More)
This paper proposes a clustered exemplar-based model for performing viewpoint invariant tracking of the 3D motion of a human subject from a single camera. Each exemplar is associated with multiple view visual information of a person and the corresponding 3D skeletal pose. The visual information takes the form of contours obtained from different viewpoints(More)
This paper presents a novel solution to the difficult task of both detecting and estimating the 3D pose of humans in monoscopic images. The approach consists of two parts. Firstly the location of a human is identified by a probabalistic assembly of detected body parts. Detectors for the face, torso and hands are learnt using adaBoost. A pose likliehood is(More)
This paper outlines a method of estimating the 3D pose of the upper human body from a single uncalibrated camera. The objective application lies in 3D Human Computer Interaction where hand depth information offers extended functionality when interacting with a 3D virtual environment, but it is equally suitable to animation and motion capture. A database of(More)
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