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Title:Dynamic object tracking and classification from a moving platform
Author(s):Lai, Andy
Advisor(s):Do, Minh N
Department / Program:Electrical & Computer Eng
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Computer vision
object recognition
object tracking
Abstract:This thesis presents an object-level SLAM system capable of tracking objects in frame and classifying stationary and moving objects. The system combines two open-source algorithms, Mask R-CNN and ORB-SLAM. Mask R-CNN provides instance-level object detection and segmentation, while ORB-SLAM provides keypoint detection, camera tracking, and local mapping. A typical SLAM system assumes a static environment and treats dynamic objects in the scene as outliers. By using object-level information from Mask R-CNN, we extend the capability to recognize and track dynamic objects. The system uses only monocular images as input, resulting in numerous potentially low-cost applications without the need for calibrating multiple sensors or using a stereo rig. This system gives a mobile agent the capability of understanding and potentially interacting with its dynamic environment.
Issue Date:2020-11-16
Rights Information:Copyright 2020 Andy Lai
Date Available in IDEALS:2021-03-05
Date Deposited:2020-12

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