Kernelized temporal cut for online temporal segmentation and recognition
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
A method for online analysis of structured processes using bayesian filters and echo state networks
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Incremental slow feature analysis with indefinite kernel for online temporal video segmentation
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Knives are picked before slices are cut: recognition through activity sequence analysis
Proceedings of the 5th international workshop on Multimedia for cooking & eating activities
Temporal segmentation and assignment of successive actions in a long-term video
Pattern Recognition Letters
Learning discriminative localization from weakly labeled data
Pattern Recognition
Max-Margin Early Event Detectors
International Journal of Computer Vision
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Automatic video segmentation and action recognition has been a long-standing problem in computer vision. Much work in the literature treats video segmentation and action recognition as two independent problems; while segmentation is often done without a temporal model of the activity, action recognition is usually performed on pre-segmented clips. In this paper we propose a novel method that avoids the limitations of the above approaches by jointly performing video segmentation and action recognition. Unlike standard approaches based on extensions of dynamic Bayesian networks, our method is based on a discriminative temporal extension of the spatial bag-of-words model that has been very popular in object recognition. The classification is performed robustly within a multi-class SVM framework whereas the inference over the segments is done efficiently with dynamic programming. Experimental results on honeybee, Weizmann, and Hollywood datasets illustrate the benefits of our approach compared to state-of-the-art methods.