Latent Pose Estimator for Continuous Action Recognition

Recently, models based on conditional random fields (CRF) have produced promising results on labeling sequential data in several scientific fields. However, in the vision task of continuous action recognition, the observations of visual features have dimensions as high as hundreds or even thousands. This might pose severe difficulties on parameter estimation and even degrade the performance. To bridge the gap between the high dimensional observations and the random fields, we propose a novel model that replace the observation layer of a traditional random fields model with a latent pose estimator. In training stage, the human pose is not observed in the action data, and the latent pose estimator is learned under the supervision of the labeled action data, instead of image-to-pose data. The advantage of this model is twofold. First, it learns to convert the high dimensional observations into more compact and informative representations. Second, it enables transfer learning to fully util...

• Type: Conference
• Year: 2008
• Where: ECCV
• Authors: Huazhong Ning, Wei Xu, Yihong Gong, Thomas S. Huang
• Keywords: Computer Vision, Conditional Random Fields, ECCV 2008, Human Pose, Latent Pose Estimator, Random Fields Model, Standard Crf Model