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.

Gori, M., Lippi, M., Maggini, M., Melacci, S. (2014). On-line Video Motion Estimation by Invariant Receptive Inputs. In 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2014 (pp.726-731). IEEE [10.1109/CVPRW.2014.112].

On-line Video Motion Estimation by Invariant Receptive Inputs

GORI, MARCO;LIPPI, MARCO;MAGGINI, MARCO;MELACCI, STEFANO
2014-01-01

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.
Gori, M., Lippi, M., Maggini, M., Melacci, S. (2014). On-line Video Motion Estimation by Invariant Receptive Inputs. In 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2014 (pp.726-731). IEEE [10.1109/CVPRW.2014.112].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11365/47104
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