Robust indoor activity recognition via boosting

In this paper, a novel statistical indoor activity recognition algorithm is introduced. While conditional random fields (CRFs) have prominent properties to this task, no optimal performance is obtained due to the fact that the performance is optimized for offline estimation. Furthermore, no previous researches provide efficient training process to optimize classifiers in onsite recognition perspective. In this paper, we propose a novel sequence estimation model suitable for online activity recognition, what we call Just-in-Time random fields (JRFs). In JRFs, efficient training and feature selection process is provided via boosting. Empirical evaluation using synthetic and real indoor activity records shows that our model drastically outperforms the previous methods in view of the classification performance with respect to the training cost.

• Type: Conference
• Year: 2008
• Where: ICPR
• Authors: Masamichi Shimosaka, Taketoshi Mori, Tomomasa Sato
• Keywords: Activity Recognition, Computer Vision, ICPR 2008, Indoor Activity, Indoor Activity Recognition