An architecture for multiple perspective interactive video
Proceedings of the third ACM international conference on Multimedia
Automatic Classification of Tennis Video for High-level Content-based Retrieval
CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
Real-Time Tracking for Enhanced Tennis Broadcasts
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A Camera-Based System for Tracking People in Real Time
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Ball Tracking and Virtual Replays for Innovative Tennis Broadcasts
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Proceedings of the conference on Visualization '01
Proceedings of the tenth ACM international conference on Multimedia
ETP '03 Proceedings of the 2003 ACM SIGMM workshop on Experiential telepresence
Multimedia Tools and Applications
Using object and trajectory analysis to facilitate indexing and retrieval of video
Knowledge-Based Systems
Determining activity patterns in retail spaces through video analysis
MM '08 Proceedings of the 16th ACM international conference on Multimedia
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As multiple video cameras and other sensors generate very large quantities of multimedia data in media productions and surveillance applications, a key challenge is to identify the relevant portions of the data and to rapidly retrieve the corresponding sensor data. Spatio-temporal activity maps serve as an efficient and intuitive graphical user interface for multimedia retrieval, particularly when the media streams are derived from multiple sensors observing a physical environment. We formulate the media retrieval problem in this context, and develop an architecture for interactive media retrieval by combining spatio-temporal "activity maps" with domain specific event information. Activity maps are computed from trajectories of motion of objects in the environment, which in turn are derived automatically by analysis of sensor data. We present an activity map based video retrieval system for the sport of tennis and demonstrate that the activity map based scheme significantly helps the user in a) discovering the relevant portions of the data, and b) non-linearly retrieving the corresponding media streams.