Motion Feature Extraction of Video and Movie Data
Abstract: Since the Video on Demand market grows at a fast rate in terms of available content and user numbers, the task arises to match personal relevant content to each individual user. This problem is tackled by implementing a recommondation system which finds relevant content by automatically detecting patterns in the individual user’s behaviour. To find such patterns, either collaborative filtering, which evaluates patterns of user groups to draw conclusions about a single user’s preferences, or content based strategies can be applied. Those content strategies analyze the watched movies of the individual user and extract quantifiable information from them. This information can be utilized to find relevant movies with similar features. The focus of this thesis lies on the extraction of motion features from movie and video data. Three feature extraction methods are presented and evaluated which classify camera movement, estimate the motion intensity and detect film transitions.
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