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Marine biologists commonly use underwater videos for their research on studying the behaviors of sea organisms.Their video analysis, however, is typically based on visual inspection. This incurs prohibitively large user costs, and severely limits the scope of biological studies. There is a need for developing vision algorithms that can address specific needs of marine biologists, such as fine-grained categorization of fish motion patterns. This is a difficult problem, because of very small inter-class and large intra-class differences between fish motion patterns. Our approach consists of three steps. First, we apply our new fish detector to identify and localize fish occurrences in each frame, under partial occlusion, and amidst dynamic texture patterns formed by whirls of sand on the sea bed. Then, we conduct tracking-by-detection. Given the similarity between fish detections,defined in terms of fish appearance and motion properties, we formulate fish tracking as transitively linking similar detections between every two consecutive frames,so as to maintain their unique track IDs. Finally, we extract histograms of fish displacements along the estimated tracks.The histograms are classified by the Random Forest technique to recognize distinct classes of fish motion patterns.Evaluation on challenging underwater videos demonstrates that our approach outperforms the state of the art. Paper Poster

under: Publications, Workshop

This is a theoretical paper that proves that probabilistic event logic (PEL) is MAP-equivalent to its conjunctive normal form (PEL-CNF). This allows us to address the NP-hard MAP inference for PEL in a principled manner.We first map the confidence-weighted formulas from a PEL knowledge base to PEL-CNF, and then conduct MAP inference for PEL-CNF using stochastic local search. Our MAP inference leverages the spanning-interval data structure for compactly representing and manipulating entire sets of time intervals without enumerating them. For experimental evaluation,we use the specific domain of volleyball videos. Our experiments demonstrate that the MAP inference for PEL-CNF successfully detects and localizes volleyball events in the face of different types of synthetic noise introduced in the ground-truth video annotations. Paper

under: Publications, Workshop

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