Vision-based obstacle detection using optical flow
Date of Publication
2006
Document Type
Bachelor's Thesis
Degree Name
Bachelor of Science in Computer Science
Subject Categories
Computer Sciences
College
College of Computer Studies
Department/Unit
Computer Science
Thesis Adviser
Joel P. Ilao
Defense Panel Member
Clement Y. Ong
Jocelynn Wong-Cu
Karlo Shane O. Campos
Abstract/Summary
Numerous robots use vision-based techniques in detecting obstacles and tracking object. As vision becomes more prominent in the field of robotics, there is a call for equally intensive research on other vision-based techniques. Optical flow is a monocular vision-based technique that is gaining attention for the past three decades.
Optical flow is a technique that derives motion from a sequence of images. This motion is embodied in the optical flow field, illustrated by a vector diagram of the movement of all pixels in the image sequence. Vision-Based Obstacle Detection Using Optical Flow adapts an existing optical flow algorithm and analyzes its importance on both real and synthethic images.
Four implementations of optical flow were tested for it's accuracy with respect to the time complexity, both the OpenCV and Barron implementation of Lucas Kanade and Horn Schunck algorithms. Among these implementations, the Lucas Kanade algorithms has the best tradeoff between accuracy and time. Time-to-collision information is computed using gradients of the velocity components of the optical flow. Using time-to-time collision information, the image was segmented into different regions, each region corresponding to an obstacle. Tests were conducted to measure the accuracy of the time-to-collision computation. Results show a large amount of errror due to the sparsity of the flow field as well as the varying values of the time-to-collision in each pixel, suggesting the use of refinement methods.
Abstract Format
html
Language
English
Format
Accession Number
TU13553
Shelf Location
Archives, The Learning Commons, 12F, Henry Sy Sr. Hall
Physical Description
1 v. (various foliations) : ill. (some col.) ; 28 cm.
Keywords
Visual perception; Optical films
Recommended Citation
Go, C. V., Medina, S. E., Navoa, R. P., & Tan, D. A. (2006). Vision-based obstacle detection using optical flow. Retrieved from https://animorepository.dlsu.edu.ph/etd_bachelors/14181