Stereo 3D Tracking of Infants in Natural Play Conditions
Published:
3D pose tracking of infants in occluding play settings
Posts by Collection
projects
Deep Learning for Fruit Segmentation
Published:
Semantic segmentation for fruit detection and counting
Dense Stereo Vision for Real Time Depth Estimation
Published:
GPU accelerated dense stereo semi global matching (CUDA) using NVIDIA TX2 , CUDA, OpenCV and OpenVX
Gesture Controlled Robot Arm
Published:
An old project from my undergrad - Arduino and Leap Motion based wireless gesture controlled robotic arm
DARPA - Fast Lightweight Autonomy
Published:
The goal of the FLA program is to explore non-traditional perception and autonomy methods that could enable a new class of algorithms for minimalistic high-speed navigation in cluttered environments.
publications
Counting Apples and Oranges with Deep Learning: A Data-Driven Approach
Published in IEEE Robotics and Automation Letters (Volume: 2, Issue: 2, April 2017), 2017
This paper describes a fruit counting pipeline based on deep learning that accurately counts fruit in unstructured environments
Recommended citation: Chen, S.W. (2017). "Counting Apples and Oranges with Deep Learning: A Data-Driven Approach" Journal 1. 1(2). https://ieeexplore.ieee.org/abstract/document/7814145/
Stereo 3D Tracking of Infants in Natural Play Conditions
Published in 2017 International Conference on Rehabilitation Robotics (ICORR), 2017
This paper describes the design and implementation of a multiple view stereoscopic 3D vision system and a supporting infant tracker pipeline..
Recommended citation: Shivakumar, S.S. (2017). "Stereo 3D Tracking of Infants in Natural Play Conditions." 2017 International Conference on Rehabilitation Robotics. 1(1). https://ieeexplore.ieee.org/document/8009353/
Unsupervised Deep Homography: A Fast and Robust Homography Estimation Model
Published in IEEE Robotics and Automation Letters (Volume: 3, Issue: 3, July 2018) , 2018
In this study, we propose an unsupervised learning algorithm that trains a Deep Convolutional Neural Network to estimate planar homographies.
Recommended citation: Ngyuen, Ty. (2018). "Unsupervised Deep Homography: A Fast and Robust Homography Estimation Model" Journal 1. 1(3). https://ieeexplore.ieee.org/document/8302515/
Monocular Camera Based Fruit Counting and Mapping with Semantic Data Association
Published in IEEE Robotics and Automation Letters (27 February 2019) , 2019
We present a cheap, lightweight, and fast fruit counting pipeline. Our pipeline relies only on a monocular camera..
Recommended citation: Liu, Xu. (2019). "Monocular Camera Based Fruit Counting and Mapping with Semantic Data Association" Journal 1. 1(3). https://ieeexplore.ieee.org/document/8653965/
Real Time Dense Depth Estimation by Fusing Stereo with Sparse Depth Measurements
Published in 2019 International Conference on Robotics and Automation (ICRA), 2019
We present an approach to depth estimation that fuses information from a stereo pair with sparse range measurements derived from a LIDAR sensor or a range camera.
Recommended citation: S. S. Shivakumar, K. Mohta, B. Pfrommer, V. Kumar and C. J. Taylor, "Real Time Dense Depth Estimation by Fusing Stereo with Sparse Depth Measurements," 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada, 2019, pp. 6482-6488. doi: 10.1109/ICRA.2019.8794023 https://ieeexplore.ieee.org/abstract/document/8794023
The Open Vision Computer: An Integrated Sensing and Compute System for Mobile Robots
Published in 2019 International Conference on Robotics and Automation (ICRA), 2019
In this paper we describe the Open Vision Computer (OVC) which was designed to support high speed, vision guided autonomous drone flight.
Recommended citation: M. Quigley et al., "The Open Vision Computer: An Integrated Sensing and Compute System for Mobile Robots," 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada, 2019, pp. 1834-1840. doi: 10.1109/ICRA.2019.8794472 https://ieeexplore.ieee.org/abstract/document/8794472
MAVNet: An Effective Semantic Segmentation Micro-Network for MAV-Based Tasks
Published in IEEE Robotics and Automation Letters ( Volume: 4 , Issue: 4 , Oct. 2019 ) , 2019
Real-time semantic image segmentation on platforms subject to size, weight, and power constraints is a key area of interest for air surveillance and inspection.
Recommended citation: T. Nguyen et al., "MAVNet: An Effective Semantic Segmentation Micro-Network for MAV-Based Tasks," in IEEE Robotics and Automation Letters, vol. 4, no. 4, pp. 3908-3915, Oct. 2019. doi: 10.1109/LRA.2019.2928734 https://ieeexplore.ieee.org/document/8764006