Detection of salient events in large datasets of underwater video




Gebali, Aleya

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NEPTUNE Canada possesses a large collection of video data for monitoring marine life. Such data is important for marine biologists who can observe species in their natural habitat on a 24/7 basis. It is counterproductive for researchers to manually search for the events of interest (EOI) in a large database. Our study aims to perform the automatic detection of the EOI de ned as animal motion. The output of this approach is in a summary video clip of the original video fi le that contains all salient events with their associated start and end frames. Our work is based on Laptev [1] spatio-temporal interest points detection method. Interest points in the 3D spatio-temporal domain (x,y,t) require frame values in local spatio-temporal volumes to have large variations along all three dimensions. These local intensity variations are captured in the magnitude of the spatio-temporal derivatives. We report experimental results on video summarization using a database of videos from Neptune Canada. The eff ect of several parameters on the performance of the proposed approach is studied in detail.



Computer Vision, Video Abstract