Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Action at a Distance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Detecting Pedestrians Using Patterns of Motion and Appearance
International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Statistical Analysis of Dynamic Actions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classification of video events using 4-dimensional time-compressed motion features
Proceedings of the 6th ACM international conference on Image and video retrieval
State-of-the-art on spatio-temporal information-based video retrieval
Pattern Recognition
Real-time human action recognition by luminance field trajectory analysis
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Overview of the MPEG-7 standard
IEEE Transactions on Circuits and Systems for Video Technology
| Hi-index | 0.00 |
Visual event detection in video streams allows easier access to, and better organization of large media collections. This paper presents an event detection framework with a novel feature that incorporates flow, appearance and trajectory information jointly. While previous event detection methods have been designed for understanding human behaviourswhere the camera is either static or with minimal motion, a more general approach is needed as real-life events are always subjected to fast camera motion and involve non-human dynamic objects. Inspired by the success of dense and overlapping orientation histograms in human detection, we build an event descriptor using orientation histograms augmented with feature point trajectory information. We put our system to test on tennis videos which have significant camera motion and multiple dynamic objects, and achieved good classification performance under a comparable setting.