A framework for automatic sports video annotation with anomaly detection and transfer learning
published: Aug. 6, 2013, recorded: April 2013, views: 3777
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
This paper describes a system that can automatically annotate videos and illustrates its application to tennis games. A unified apparatus is proposed, cast in a Bayesian reasoning framework. This is supported by a cognitive memory architecture that allows the system to store raw video data at the lowest cognitive level and its semantic annotation with increasing levels of abstraction up to determining the score of a game. Also embedded in the system is a set of mechanisms to detect anomalies caused by a change of domain in the input data. Once an anomaly is detected, transfer learning methods are triggered to adapt the knowledge to new domains, such as new sport modalities. We also present a generic framework for rule induction that is crucial in the context of an adaptive annotation system.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !