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Fine-grained Categorization of Fish Motion Patterns in Underwater Videos (ICCV 2011)

Posted by: | December 10, 2011 | 1 Comment |

Marine biologists commonly use underwater videos fortheir research on studying the behaviors of sea organisms.Their video analysis, however, is typically based on visualinspection. This incurs prohibitively large user costs, andseverely limits the scope of biological studies. There is aneed for developing vision algorithms that can address specificneeds of marine biologists, such as fine-grained categorizationof fish motion patterns. This is a difficult problem, because of very small inter-class and large intra-classdifferences between fish motion patterns. Our approachconsists of three steps. First, we apply our new fish detectorto identify and localize fish occurrences in each frame, underpartial occlusion, and amidst dynamic texture patternsformed by whirls of sand on the sea bed. Then, we conducttracking-by-detection. Given the similarity between fish detections,defined in terms of fish appearance and motionproperties, we formulate fish tracking as transitively linkingsimilar detections between every two consecutive frames,so as to maintain their unique track IDs. Finally, we extracthistograms of fish displacements along the estimated tracks.The histograms are classified by the Random Forest techniqueto recognize distinct classes of fish motion patterns.Evaluation on challenging underwater videos demonstratesthat our approach outperforms the state of the art. Paper Poster

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