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On the use of 3D Gray Code Kernels for motion-related tasks in videos



On the use of 3D Gray Code Kernels for motion-related tasks in videos


Elena Nicora - DIBRIS, University of Genoa


In order to solve many problems in Computer Vision, current state-of-art approaches leverage the use of deep architectures, whose impressive results are provided to the price of dramatic requirements in terms of data and computational resources. Traditional approaches may come in hand by pursuing efficiency and portability at the expense of a less precise result. In this seminar I will present the Gray-Code Kernels (GCKs), a family of filter kernels that can be employed as a highly efficient filtering scheme, used in literature almost exclusively for fast 2D pattern matching. GCKs were originally designed so that successive convolutions of an image with a set of such filters require only two operations per pixel, thus cutting down the execution time with respect to classical convolutions. In this talk I will discuss how 3D GCKs may be exploited to efficiently gather meaningful spatio-temporal cues from videos, useful in motion-based saliency detection problems. I will also discuss the potentials of the method for application to motion segmentation and classification problems.


Elena is a third year PhD student working in the Machine Learning & Vision unit at MaLGa, under the supervision of Nicoletta Noceti. Her research interests include DL and traditional methods for various motion understanding related tasks, such as motion detection, motion-based segmentation and human action classification.


2021-04-27 at 3:00 pm