13 research outputs found
Rule Of Thumb: Deep derotation for improved fingertip detection
We investigate a novel global orientation regression approach for articulated
objects using a deep convolutional neural network. This is integrated with an
in-plane image derotation scheme, DeROT, to tackle the problem of per-frame
fingertip detection in depth images. The method reduces the complexity of
learning in the space of articulated poses which is demonstrated by using two
distinct state-of-the-art learning based hand pose estimation methods applied
to fingertip detection. Significant classification improvements are shown over
the baseline implementation. Our framework involves no tracking, kinematic
constraints or explicit prior model of the articulated object in hand. To
support our approach we also describe a new pipeline for high accuracy magnetic
annotation and labeling of objects imaged by a depth camera.Comment: To be published in proceedings of BMVC 201
CoBe -- Coded Beacons for Localization, Object Tracking, and SLAM Augmentation
This paper presents a novel beacon light coding protocol, which enables fast
and accurate identification of the beacons in an image. The protocol is
provably robust to a predefined set of detection and decoding errors, and does
not require any synchronization between the beacons themselves and the optical
sensor. A detailed guide is then given for developing an optical tracking and
localization system, which is based on the suggested protocol and readily
available hardware. Such a system operates either as a standalone system for
recovering the six degrees of freedom of fast moving objects, or integrated
with existing SLAM pipelines providing them with error-free and easily
identifiable landmarks. Based on this guide, we implemented a low-cost
positional tracking system which can run in real-time on an IoT board. We
evaluate our system's accuracy and compare it to other popular methods which
utilize the same optical hardware, in experiments where the ground truth is
known. A companion video containing multiple real-world experiments
demonstrates the accuracy, speed, and applicability of the proposed system in a
wide range of environments and real-world tasks. Open source code is provided
to encourage further development of low-cost localization systems integrating
the suggested technology at its navigation core