Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
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
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
A support vector method for multivariate performance measures
ICML '05 Proceedings of the 22nd international conference on Machine learning
Beyond Tracking: Modelling Activity and Understanding Behaviour
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human Action Recognition by Semilatent Topic Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting unusual activity in video
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
International Journal of Computational Vision and Robotics
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We develop methods for action retrieval from surveillance video using contextual feature representations. The novelty of our proposed approach is two-fold. First, we introduce a new feature representation called the action context (AC) descriptor. The AC descriptor encodes information about not only the action of an individual person in the video, but also the behaviour of other people nearby. This feature representation is inspired by the fact that the context of what other people are doing provides very useful cues for recognizing the actions of each individual. Second, we formulate our problem as a retrieval/ranking task, which is different from previous work on action classification. We develop an action retrieval technique based on rank-SVM, a state-of-the-art approach for solving ranking problems. We apply our proposed approach on two real-world datasets. The first dataset consists of videos of multiple people performing several group activities. The second dataset consists of surveillance videos from a nursing home environment. Our experimental results show the advantage of using contextual information for disambiguating different actions and the benefit of using rank-SVMs instead of regular SVMs for video retrieval problems.