This paper describes a vision-based street detection algorithm to be used by small autonomous aircraft in low-altitude
urban surveillance. The algorithm uses Bayesian analysis to differentiate between street and background pixels. The
color profile of edges on the detected street is used to represent objects with respect to their surroundings. These color
profiles are used to improve street detection over time. Pixels that do not likely originate from the "true" street are
excluded from the recurring Bayesian estimation in the video. Results are presented comparing to a previously published
Unmanned Aerial Vehicle (UAV) road detection algorithm. Robust performance is demonstrated with urban surveillance
scenes including UAV surveillance, police chases from helicopters, and traffic monitoring. The proposed method is
shown to be robust to data uncertainty and has low sensitivity to the training dataset. Performance is computed using a
challenging multi-site dataset that includes compression artifacts, poor resolution, and large variation of scene
complexity.
This paper presents an automated classification system for images based on their visual complexity. The image complexity is approximated using a clutter measure, and parameters for processing it are dynamically chosen. The classification method is part of a vision-based collision avoidance system for low altitude aerial vehicles, intended to be used during search and rescue operations in urban settings. The collision avoidance system focuses on detecting thin obstacles such as wires and power lines. Automatic parameter selection for edge detection shows a 5% and 12% performance improvement for medium and heavily cluttered images respectively. The automatic classification enabled the algorithm to identify near invisible power lines in a 60 frame video footage from a SUAV helicopter crashing during a search and rescue mission at hurricane Katrina, without any manual intervention.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.