Article
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Motion Superpixels for Temporal Video Classification
Version 1
: Received: 22 September 2018 / Approved: 24 September 2018 / Online: 24 September 2018 (09:54:01 CEST)
How to cite: Yudistira, N.; Kurita, T. Motion Superpixels for Temporal Video Classification. Preprints 2018, 2018090449. https://doi.org/10.20944/preprints201809.0449.v1 Yudistira, N.; Kurita, T. Motion Superpixels for Temporal Video Classification. Preprints 2018, 2018090449. https://doi.org/10.20944/preprints201809.0449.v1
Abstract
Superpixels are a representation of still images as pixel grids because of their more meaningful information compared with atomic pixels. However, their usefulness for video classification has been given little attention. In this paper, rather than using spatial RGB values as low-level features, we use optical flows mapped into hue-saturation-value (HSV) space to capture rich motion features over time. We introduce motion superpixels, which are superpixels generated from flow fields. After mapping flow fields into HSV space, independent superpixels are formed by iteration of seeded regions. Every grid of a motion superpixel is tracked over time using nearest neighbors in the histogram of flow (HOF) for consecutive flow fields. To define the temporal representation, the evolution of three features within the superpixel region, namely the HOF, HOG, and the center of superpixel mass are used as descriptors. The bag of features algorithm is used to quantify final features, and generalized histogram-kernel support vector machines are used as learning algorithms. We evaluate the proposed superpixel tracking on first-person videos and action sports videos.
Keywords
motion; superpixel; temporal features; video classification
Subject
Engineering, Electrical and Electronic Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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