Classifier Boosting for Human Activity Recognition

Abstract

The ability to visually infer human activities happening in an environment is becoming increasingly important due to the tremendous practical applications it offers [1]. Systems that can automatically recognize human activities can potentially help us in monitoring people’s health as they age [7], and to fight crime through improved surveillance [26]. They have tremendous medical applications in terms of helping surgeons perform better by identifying and evaluating crucial parts of the surgical procedures, and providing the medical specialists with useful feedback [2]. Similarly, these systems can help us improve our productivity in office environments by detecting various interesting and important events around us to enhance our involvement in important office tasks [21].