Human activity and behavior analysis and classification with applications

The aim is to analyze different activities and behaviors from tracked objects (e.g. humans, faces, machine tools) from videos.  Some typical examples of applications are in: elderly care centers or out-patient care units, office environments, vehicle drivers, and automations.

The study includes the machine recognition of different (or, specific) activities/behaviors, analysis of individual activities to obtain a range of long-term (or short-term) statistics on normal/abnormal activities/behaviors, for improving elderly care, detecting abnormality, improving office environment, and reducing driving risk. Main methods we investigate in this project are:

• Novel machine learning and pattern classification methods, e.g., domain-shift classifiers on manifolds, AdaBoost, SVMs;
• Effective feature descriptors for activities/behaviors;
• Statistical modeling and parameter estimations for normal/abnormal activities;
• Generate recommendations or trigger actions based on objective criteria.


Page manager Published: Mon 28 Oct 2013.