Object tracking and detection in dynamic scenes
at ORAU Workforce Solutions

Date Posted: 5/14/2019

Opportunity Description

Title of Research Opportunity: Object tracking and detection in dynamic scenes Future autonomous systems will need to operate independently in highly dynamic environments with minimal supervision. This project will focus on multi-modal (at least color+depth) approaches to dynamic object tracking on mobile platforms. A particular point of emphasis will be to develop methods to automatically label data for learning, thus reducing human annotation effort. The end goal is to develop a series of capabilities such as object tracking and object proposal generation which support highly dynamic scenes. The underlying robot framework is ROS, while Tensorflow and PCL will be used for 2D and 3D perception. Required: • Linux proficiency • C++ and/or Python proficiency • ROS familiarity Desired: • Experience with dynamic tracking algorithms • PCL proficiency • TensorFlow or other deep learning architecture proficiency • Hands-on robotics experience Keywords: robotics, tracking, computer vision, ROS, PCL, RGBD

Opportunity Snapshot

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