Dynamic object tracking and classification from a moving platform
Lai, Andy
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https://hdl.handle.net/2142/109359
Description
Title
Dynamic object tracking and classification from a moving platform
Author(s)
Lai, Andy
Issue Date
2020-11-16
Director of Research (if dissertation) or Advisor (if thesis)
Do, Minh N
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Computer vision
object recognition
object tracking
SLAM
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.
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