Abstract
We developed an automatic behavior labeling system for tremendous data such as life-logs gathered through sensing human life. In this paper, the following advantages of the system were emphasized: 1) labeling behavior exploiting human knowledge and experience 2) relaxing constraints and selecting features freely as to constructing a labeling model, 3) helping find effective features for each behavior label, 4) dealing with incomplete data efficiently. The sequential labeling model with above features could be realized using Conditional Random Field (CRF) framework. We describe features of CRF, procedures of selecting effective features and how to deal with incomplete data in CRF framework. Lastly the model with CRF framework was applied to the real sensing environment and we state the performance based on the model and the feasibility of CRF framework in the human behavior recognition fields.
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