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Learning and Recognizing Activities in Streams of Video



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Learning and Recognizing Activities in Streams of Video Dinesh Govindaraju and Manuela Veloso Computer Science Department Carnegie Mellon University dineshg cmu edu veloso cs cmu edu Abstract 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 versatile as the learning is applicable to a wide variety of data and is not constrained by particular aspects of a specific problem This paper is structured as follows In the next section we define the problem in more detail and identify some specific problems that arise when generating a solution We then outline the approach taken by our algorithm and present some results by evaluating it against a hand labeled control case We finally discuss the results of the evaluation and some possible future improvements to the algorithm Introduction Effective behavior recognition using a Camera Assisted Meeting Event Observer 1 in the context of an office meeting is a challenging problem with many potential benefits to be reaped If when given a video sequence of an office meeting we are able to accurately segment the video and recognize consistent activities of meeting attendees we would then be able to augment higher level functionality such as recognizing behaviors and with these infer which attendees are presenting agenda items and even gauge attendee interest levels during the meeting Previous work done in activity recognition includes using optical flow 8 12 as well as variants of HMMs 7 to carry out the recognition Instances



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