Semi-Latent Dirichlet Allocation: A Hierarchical Model for Human Action Recognition

We propose a new method for human action recognition from video sequences using latent topic models. Video sequences are represented by a novel “bag-of-words” representation, where each frame corresponds to a “word”. The major difference between our model and previous latent topic models for recognition problems in computer vision is that, our model is trained in a “semi-supervised” way. Our model has several advantages over other similar models. First of all, the training is much easier due to the decoupling of the model parameters. Secondly, it naturally solves the problem of how to choose the appropriate number of latent topics. Thirdly, it achieves much better performance by utilizing the information provided by the class labels in the training set. We present action classification and irregularity detection results, and show improvement over previous methods.

• Type: Conference
• Year: 2007
• Where: HUMO
• Authors: Yang Wang 0003, Payam Sabzmeydani, Greg Mori
• Keywords: Computer Animation, HUMO 2007, Latent Topic Models, Latent Topics, Video Sequences