Learning a discriminative hidden part model for human action recognition

We present a discriminative part-based approach for human action recognition from video sequences using motion features. Our model is based on the recently proposed hidden conditional random field (hCRF) for object recognition. Similar to hCRF for object recognition, we model a human action by a flexible constellation of parts conditioned on image observations. Different from object recognition, our model combines both large-scale global features and local patch features to distinguish various actions. Our experimental results show that our model is comparable to other state-of-the-art approaches in action recognition. In particular, our experimental results demonstrate that combining large-scale global features and local patch features performs significantly better than directly applying hCRF on local patches alone.

• Type: Conference
• Year: 2008
• Where: NIPS
• Authors: Yang Wang 0003, Greg Mori
• Keywords: Information Technology, Large-scale Global Features, Local Patch Features, NIPS 2008, Object Recognition