F
Federico Pernici
Researcher at University of Florence
Publications - 56
Citations - 828
Federico Pernici is an academic researcher from University of Florence. The author has contributed to research in topics: Pan–tilt–zoom camera & Camera auto-calibration. The author has an hindex of 14, co-authored 53 publications receiving 695 citations.
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Journal ArticleDOI
Object Tracking by Oversampling Local Features.
TL;DR: This paper presents the ALIEN tracking method that exploits oversampling of local invariant representations to build a robust object/context discriminative classifier and shows that the learning rule has asymptotic stability under mild conditions and confirms the drift-free capability of the method in long-term tracking.
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Metric 3D reconstruction and texture acquisition of surfaces of revolution from a single uncalibrated view
TL;DR: This paper addresses the problem of metric reconstruction and texture acquisition from a single uncalibrated view of a surface of revolution (SOR) by exploiting the analogy with the geometry of single axis motion and exploits both the geometric and topological properties of the transformation that relates the apparent contour to the SOR scaling function.
Journal ArticleDOI
Exploiting distinctive visual landmark maps in pan-tilt-zoom camera networks
TL;DR: This paper describes a novel framework exploiting a PTZ camera network to achieve high accuracy in the task of relating the feet position of a person in the image of the master camera, to his head position in theimage of the slave camera.
Proceedings ArticleDOI
Memory Based Online Learning of Deep Representations from Video Streams
TL;DR: It is shown that the proposed learning procedure is asymptotically stable and can be effectively used in relevant applications like multiple face identification and tracking from unconstrained video streams.
Journal ArticleDOI
Towards on-line saccade planning for high-resolution image sensing
TL;DR: The whole problem of designing an active observer to plan a sequence of decisions regarding what target to look at, through a foveal-sensing action can be solved by modelling the attentional gaze control as a novel on-line dynamic vehicle routing problem (DVRP) with deadlines.