ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Searching for Complex Human Activities with No Visual Examples
International Journal of Computer Vision
Human activity analysis: A review
ACM Computing Surveys (CSUR)
Human detection using oriented histograms of flow and appearance
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Evaluating knowledge transfer and zero-shot learning in a large-scale setting
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Joint segmentation and classification of human actions in video
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Action recognition by dense trajectories
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
A database for fine grained activity detection of cooking activities
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Human activity prediction: Early recognition of ongoing activities from streaming videos
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Recognizing ingredients at cutting process by integrating multimodal features
Proceedings of the ACM multimedia 2012 workshop on Multimedia for cooking and eating activities
On recognizing actions in still images via multiple features
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
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In this paper, we introduce a model to classify cooking activities using their visual and temporal coherence information. We fuse multiple feature descriptors for fine-grained activity recognition as we would need every single detail to catch even subtle differences between classes with low inter-class variance. Considering the observation that daily activities such as cooking are likely to be performed in sequential patterns of activities, we also model temporal coherence of activities. By combining both aspects, we show that we can improve the overall accuracy of cooking recognition tasks.