In this paper, we address the problem of estimating the optical flow in long-term video sequences. We devise a computational scheme that exploits the idea of receptive fields, in which the pixel flow does not only depends on the brightness level of the pixel itself, but also on neighborhood-related information. Our approach relies on the definition of receptive units that are invariant to affine transformations of the input data. This distinguishing characteristic allows us to build a video-receptive-inputs database with arbitrary detail level, that can be used to match local features and to determine their motion. We propose a parallel computational scheme, well suited for nowadays parallel architectures, to exploit motion information and invariant features from real-time video streams, for deep feature extraction, object detection, tracking, and other applications.

On-line video motion estimation by invariant receptive inputs / LIPPI, MARCO. - ELETTRONICO. - (2014), pp. 726-731. (Intervento presentato al convegno 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2014 tenutosi a usa nel 2014) [10.1109/CVPRW.2014.112].

On-line video motion estimation by invariant receptive inputs

LIPPI, MARCO
2014

Abstract

In this paper, we address the problem of estimating the optical flow in long-term video sequences. We devise a computational scheme that exploits the idea of receptive fields, in which the pixel flow does not only depends on the brightness level of the pixel itself, but also on neighborhood-related information. Our approach relies on the definition of receptive units that are invariant to affine transformations of the input data. This distinguishing characteristic allows us to build a video-receptive-inputs database with arbitrary detail level, that can be used to match local features and to determine their motion. We propose a parallel computational scheme, well suited for nowadays parallel architectures, to exploit motion information and invariant features from real-time video streams, for deep feature extraction, object detection, tracking, and other applications.
2014
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2014
usa
2014
LIPPI, MARCO
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1356476
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