Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Automated Analysis of Nursing Home Observations
IEEE Pervasive Computing
Hidden Conditional Random Fields for Gesture Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Conditional models for contextual human motion recognition
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Hidden Conditional Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast food recognition from videos of eating for calorie estimation
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
PFID: pittsburgh fast-food image dataset
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Efficiently inducing features of conditional random fields
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Hi-index | 0.00 |
The drinking activity is a common event in a fast food eating process. In this paper, we present a study on drinking activity analysis from fast food eating video using generative models. We apply three different generative models, namely Conditional Random Field (CRF), Hidden-state Conditional Random Field (HCRF), and Latent-Dynamic Conditional Random Field (LDCRF), to characterize drinking activities in a fast food eating process. The CRF and LDCRF models are applied in the frame and sequence level classification while HCRF model is used on video clip classification. We evaluate the proposed method on a dataset that contains 27 videos from 9 fast food restaurants. Experimental results show that the proposed method can obtain promising classification results for drinking activity labeling and classification, averagely achieving accuracies of 79.80% for frame and 72.81% for subsequence. And the CRF model outperforms LDCRF and HCRF models.