Human motion analysis: a review
Computer Vision and Image Understanding
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
The Journal of Machine Learning Research
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Bayesian Hierarchical Model for Learning Natural Scene Categories
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Discovering Objects and their Localization in Images
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Learning Object Categories from Google"s Image Search
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
The Function Space of an Activity
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
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
Behavior recognition via sparse spatio-temporal features
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Representing shape with a spatial pyramid kernel
Proceedings of the 6th ACM international conference on Image and video retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding
Opinion integration through semi-supervised topic modeling
Proceedings of the 17th international conference on World Wide Web
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
Human Activity Recognition with Metric Learning
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Space-Time Shapelets for Action Recognition
WMVC '08 Proceedings of the 2008 IEEE Workshop on Motion and video Computing
Human action recognition by feature-reduced Gaussian process classification
Pattern Recognition Letters
Action categorization with modified hidden conditional random field
Pattern Recognition
Human Action Recognition by Semilatent Topic Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Classification via Semi-supervised pLSA
ICIG '09 Proceedings of the 2009 Fifth International Conference on Image and Graphics
A survey on vision-based human action recognition
Image and Vision Computing
Action categorization by structural probabilistic latent semantic analysis
Computer Vision and Image Understanding
2LDA: Segmentation for Recognition
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Semi-supervised PLSA for Document Clustering
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Biomedical time series clustering based on non-negative sparse coding and probabilistic topic model
Computer Methods and Programs in Biomedicine
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Latent topic models such as Latent Dirichlet Allocation (LDA) and probabilistic Latent Semantic Analysis (pLSA) have demonstrated success in computer vision tasks. Most existing approaches train LDA and pLSA in an unsupervised manner, where the training data does not include any class label information. However, the class labels in training data are very important for the task of classification. In this paper, we propose to train a pLSA model in a supervised manner for the task of human motion analysis using the bag-of-words representation. Each frame in a video is treated as a word, and all the frames in the training videos are clustered to construct a codebook. The class label information is used to learn the pLSA model in a supervised manner, which not only makes the training more efficient, but also improves the overall recognition accuracy significantly. In addition, we employ the pyramid Histogram of orientation Gradient (HoG) to encode a human figure in each frame. The pyramid HoG descriptor does not require extraction of silhouettes, and is invariant to translations and rotations to some extent. The method is validated using two standard datasets. The experimental results show that our method can accurately recognize human motion in video sequences. Moreover, the overall recognition accuracy is rather stable with respect to the codebook size.