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Automatic detection and tracking in underwater environments with marine snow

University of British Columbia

This project addresses the issue of automatic detection and tracking of man-made objects in subsea environments with poor visibility and marine snow. Underwater research and engineering is a quickly growing field and there are few computer vision techniques that specifically address these challenges. The proposed system involves minimizing noise and video artifacts, estimating camera motion, detecting line segments and tracking targets. Overall, the system performs well under the conditions in the test video and the equal error rate is approximately 16%. Tests show how parameters may be tuned to account for changes in environmental conditions and to trade off the number of false negatives and false positives. System performance is affected by many factors. Poorest performance occurs under conditions of heavy marine show, low-contrast targets, and fast camera motion. Performance also suffers if the background conditions in the image change. This research makes two contributions. First, we provide a survey of techniques that address similar problems and evaluate their suitability for this application, Second, we integrate existing techniques into a larger system. Techniques include median filtering, Canny edge detection, Hough transforms, Lucas-Kanade first-order optical flow and particle filtering. Where gaps exist between system components, new methods are developed. Testing evaluates the effects of system parameters and the conditions under which the system is effective.

Text

2010-12-16

Attribution-ShareAlike 3.0 Unported

http://hdl.handle.net/2429/30440

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-sa/3.0/