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arXiv · 1612.05332

Fast, Dense Feature SDM on an iPhone

Ashton Fagg·Simon Lucey·Sridha Sridharan
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Abstract

In this paper, we present our method for enabling dense SDM to run at over 90 FPS on a mobile device. Our contributions are two-fold. Drawing inspiration from the FFT, we propose a Sparse Compositional Regression (SCR) framework, which enables a significant speed up over classical dense regressors. Second, we propose a binary approximation to SIFT features. Binary Approximated SIFT (BASIFT) features, which are a computationally efficient approximation to SIFT, a commonly used feature with SDM. We demonstrate the performance of our algorithm on an iPhone 7, and show that we achieve similar accuracy to SDM.

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Ashton Fagg, Simon Lucey, Sridha Sridharan. 2016-12-16. Fast, Dense Feature SDM on an iPhone. https://arxiv.org/abs/1612.05332

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