Video-based activity monitoring for indoor environments

In this work, we study how continuous video monitoring and intelligent video processing can be used in eldercare to assist the independent living of elders and to improve the efficiency of eldercare practice. More specifically, we construct an advanced silhouette extraction and tracking algorithm for indoor environments. An adaptive learning method was developed to estimate the physical location and moving speed of a person from a single camera view without calibration. Then hierarchical decision tree and dimension reduction methods were used for human action recognition. We extract important ADL (activities of daily living) statistics for automated functional assessment. Our extensive tests over these massive video datasets demonstrate that the proposed automated activity analysis system is very efficient.