In this paper, we address the problem of estimating the optical flow in long-term video sequences. We devise a com- putational 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 / Gori M.; Lippi M.; Maggini M.; Melacci S.. - ELETTRONICO. - (2014), pp. 712-717. (Intervento presentato al convegno IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014) tenutosi a Columbus, Ohio US nel June 24-27, 2014).

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 com- putational 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
The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops
712
717
On-line Video Motion Estimation by Invariant Receptive Inputs / Gori M.; Lippi M.; Maggini M.; Melacci S.. - ELETTRONICO. - (2014), pp. 712-717. (Intervento presentato al convegno IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014) tenutosi a Columbus, Ohio US nel June 24-27, 2014).
Gori M.; Lippi M.; Maggini M.; Melacci S.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/394777
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