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Erschienen in:
Bayesian Semantic Instance Segmentation in Open Set World Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Motion Feature Network: Fixed Motion Filter for Action Recognition

verfasst von : Myunggi Lee, Seungeui Lee, Sungjoon Son, Gyutae Park, Nojun Kwak

Erschienen in: Computer Vision – ECCV 2018

Verlag: Springer International Publishing

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Abstract

Spatio-temporal representations in frame sequences play an important role in the task of action recognition. Previously, a method of using optical flow as a temporal information in combination with a set of RGB images that contain spatial information has shown great performance enhancement in the action recognition tasks. However, it has an expensive computational cost and requires two-stream (RGB and optical flow) framework. In this paper, we propose MFNet (Motion Feature Network) containing motion blocks which make it possible to encode spatio-temporal information between adjacent frames in a unified network that can be trained end-to-end. The motion block can be attached to any existing CNN-based action recognition frameworks with only a small additional cost. We evaluated our network on two of the action recognition datasets (Jester and Something-Something) and achieved competitive performances for both datasets by training the networks from scratch.

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Vorheriges Kapitel The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking
Nächstes Kapitel Efficient Sliding Window Computation for NN-Based Template Matching
Fußnoten
1
We use the term hierarchy to represent the level of abstraction. A layer or a block of layers can correspond to a hierarchy.
 
2
For convenience, here, we use the notation \(F_t\) and \(G_{t+\varDelta t}\) instead of F(t) and \(G(t+\varDelta t)\). The meaning of a subscript will be obvious in the context.
 
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Metadaten
Titel
Motion Feature Network: Fixed Motion Filter for Action Recognition
verfasst von
Myunggi Lee
Seungeui Lee
Sungjoon Son
Gyutae Park
Nojun Kwak
Copyright-Jahr
2018
Buch
Computer Vision – ECCV 2018

Print ISBN: 978-3-030-01248-9

Electronic ISBN: 978-3-030-01249-6

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-030-01249-6_24

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