Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Pictorial Structures for Object Recognition
International Journal of Computer Vision
International Journal of Computer Vision
Learning structural SVMs with latent variables
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling temporal structure of decomposable motion segments for activity classification
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Human activity analysis: A review
ACM Computing Surveys (CSUR)
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Learning a 3D Human Pose Distance Metric from Geometric Pose Descriptor
IEEE Transactions on Visualization and Computer Graphics
Real-time human pose recognition in parts from single depth images
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Human activity prediction: Early recognition of ongoing activities from streaming videos
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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In this paper, we propose to recognize unsuccessful activities (e.g., one tries to dress himself but fails), which have much more complex temporal structures, as we don't know when the activity performer fails (which is called the point of failure in this paper). We develop a two-layer latent structural SVM model to tackle this problem: the first layer specifies the point of failure, and the second layer specifies the temporal positions of a number of key stages accordingly. The stages before the point of failure are successful stages, while the stages after the point of failure are background stages. Given weakly labeled training data, our training algorithm alternates between inferring the two-layer latent structure and updating the structural SVM parameters. In recognition, our method can not only recognize unsuccessful activities, but also infer the latent structure. We demonstrate the effectiveness of our proposed method on several newly collected datasets.