Learning and Recognizing Activities in Streams of Video

This paper presents an algorithm for learning the underlying models which generate streams of observations, found in video data, which encode activities performed by a person who appears in the video. With these learned models, we then aim to carry out recognition in new video streams which display the same activities as the ones that were learned. Our algorithm represents the underlying models as regular Hidden Markov Models as the problem includes sequential and temporally discrete observations and uses the Baum Welch algorithm in learning the underlying models.

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