Person-on-Person Violence Detection in Video Data
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
MILES: Multiple-Instance Learning via Embedded Instance Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Simultaneous tracking of multiple body parts of interacting persons
Computer Vision and Image Understanding
Human Action Recognition in Videos Using Kinematic Features and Multiple Instance Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object, scene and actions: combining multiple features for human action recognition
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Handling label noise in video classification via multiple instance learning
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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We address the problem of categorizing turn-taking interactions between individuals. Social interactions are characterized by turn-taking and arise frequently in real-world videos. Our approach is based on the use of temporal causal analysis to decompose a space-time visual word representation of video into co-occuring independent segments, called causal sets [1]. These causal sets then serves the input to a multiple instance learning framework to categorize turn-taking interactions. We introduce a new turn-taking interactions dataset consisting of social games and sports rallies. We demonstrate that our formulation of multiple instance learning (QP-MISVM) is better able to leverage the repetitive structure in turn-taking interactions and demonstrates superior performance relative to a conventional bag of words model.