Incremental learning approach for human detection and tracking

Abstract

Human detection is a key functionality to reach Human Robot/Computer Interaction. The human tracking is also a rapidly evolving area in computer and robot vision; it aims to explore and to follow human motion. We present in this article an intelligent system to learn human detection. The descriptors used in our system make up the combination of HOG and SIFT that capture salient features of humans automatically. Additionally, an incremental PCA is employed to follow the detected humans. Experimental results have been extracted for a set of sequences with standing and moving people at different positions and with a variation of backgrounds.