@inproceedings{77c56e0462ac49dfb37857f545203877,
title = "Direct multichannel tracking",
abstract = "We present direct multichannel tracking, an algorithm for tracking the pose of a monocular camera (visual odometry) using high-dimensional features in a direct image alignment framework. Instead of using a single grayscale channel and assuming intensity constancy as in existing approaches, we extract multichannel features at each pixel from each image and assume feature constancy among consecutive images. High-dimensional features are more discriminative and robust to noise and image variations than intensities, enabling more accurate camera tracking. We demonstrate our claim using conventional hand-crafted features such as SIFT as well as more recent features extracted from convolutional neural networks (CNNs) such as Siamese and AlexNet networks. We evaluate the performance of our algorithm against the baseline case (single-channel tracking) using several public datasets, where the AlexNet feature provides the best pose estimation results.",
keywords = "3D-reconstruction, CNN-features, Camera-Tracking, Camera-pose-estimation, Computer-vision, Dense-SIFT, Direct-method, Monocular-vision, Multichannel, Visual-odometry",
author = "Carlos Jaramillo and Yuichi Taguchi and Chen Feng",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 7th IEEE International Conference on 3D Vision, 3DV 2017 ; Conference date: 10-10-2017 Through 12-10-2017",
year = "2018",
month = may,
day = "25",
doi = "10.1109/3DV.2017.00047",
language = "English (US)",
series = "Proceedings - 2017 International Conference on 3D Vision, 3DV 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "347--355",
booktitle = "Proceedings - 2017 International Conference on 3D Vision, 3DV 2017",
}