Thermal Image-Based Deer Detection to Reduce Accidents Due to Deer-Vehicle Collisions
Deer-vehicle collision (DVC) is one of the most serious traffic issues in the Unite States. To reduce DVCs, this research developed a system using a contour-based histogram of oriented gradients algorithm (CNT-HOG) to identify deer through the processing of images taken by thermographic cameras. The system is capable of detecting deer in low visibility. Two motors are applied to enlarge the detection range and make the system capable of tracking deer by providing two degrees of freedom. The main assumption in the CNT-HOG algorithm is that the deer are brighter than their background in a thermo image. The brighter areas are defined as the regions of interest, or ROIs. ROIs were identified based on the contours of brighter areas. HOG features were then collected and certain detection frameworks were applied to the image portions in the ROIs instead of the whole image. In the detection framework, a Linear Support Vector Machine classifier was applied to achieve identification. The system has been tested in various scenarios, such as a zoo and roadways in different weather conditions. The influence of the visible percentage of a deer body and the posture of a deer on detection accuracy has been measured. The results of the applications on roadside have shown that this system can achieve high detection accuracy (up to 100%) with fast computation speed (10 Hz). Achieving such a goal will help to decrease the occurrence of DVCs on roadsides.
- Record URL:
- Summary URL:
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Supplemental Notes:
- This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
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Corporate Authors:
University of Minnesota, Duluth
Department of Mechanical and Industrial Engineering, 10 University Drive
Duluth, MN United States 55812Intelligent Transportation Systems Institute
200 Transportation and Safety Building
511 Washington Avenue, S.W.
Minneapolis, MN United States 55455Research and Innovative Technology Administration
1200 New Jersey Avenue, SE
Washington, DC United States 20590 -
Authors:
- Zhou, Debao
- Publication Date: 2013-1
Language
- English
Media Info
- Media Type: Digital/other
- Edition: Final Report
- Features: Appendices; Figures; Photos; References; Tables;
- Pagination: 67p
Subject/Index Terms
- TRT Terms: Accuracy; Algorithms; Crash avoidance systems; Crashes; Deer; Detection and identification systems; Thermal imagery; Wildlife
- Subject Areas: Highways; Safety and Human Factors; I85: Safety Devices used in Transport Infrastructure;
Filing Info
- Accession Number: 01472553
- Record Type: Publication
- Report/Paper Numbers: CTS 13-06
- Files: UTC, TRIS, ATRI, USDOT
- Created Date: Feb 19 2013 8:47AM