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2018 | OriginalPaper | Chapter

Depth-Adaptive Computational Policies for Efficient Visual Tracking

Authors : Chris Ying, Katerina Fragkiadaki

Published in: Energy Minimization Methods in Computer Vision and Pattern Recognition

Publisher: Springer International Publishing

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Abstract

Current convolutional neural networks algorithms for video object tracking spend the same amount of computation for each object and video frame [3]. However, it is harder to track an object in some frames than others, due to the varying amount of clutter, scene complexity, amount of motion, and object’s distinctiveness against its background. We propose a depth-adaptive convolutional siamese network that performs video tracking adaptively at multiple neural network depths. Parametric gating functions are trained to control the depth of the convolutional feature extractor by minimizing a joint loss of computational cost and tracking error. Our network achieves accuracy comparable to the state-of-the-art on the VOT2016 benchmark. Furthermore, our adaptive depth computation achieves higher accuracy for a given computational cost than traditional fixed-structure neural networks. The presented framework extends to other tasks that use convolutional neural networks and enables trading speed for accuracy at runtime.

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previous chapter Temporal Semantic Motion Segmentation Using Spatio Temporal Optimization
next chapter Multiframe Motion Coupling for Video Super Resolution
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Metadata
Title
Depth-Adaptive Computational Policies for Efficient Visual Tracking
Authors
Chris Ying
Katerina Fragkiadaki
Copyright Year
2018
Book
Energy Minimization Methods in Computer Vision and Pattern Recognition

Print ISBN: 978-3-319-78198-3

Electronic ISBN: 978-3-319-78199-0

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-78199-0_8

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