Fast, reliable, adaptive, bimodal people tracking for indoor environments

Abstract

We present a real-time system for a mobile robot that can reliably detect and track people in uncontrolled indoor environments. The system uses a combination of leg detection based on distance information from a laser range sensor and visual face detection based on an analogical algorithm implemented on specialized hardware (the CNN universal machine). Results from tests in a variety of environments with different lighting conditions, a different number of appearing and disappearing people, and different obstacles are reported to demonstrate that the system can find and subsequently track several, possibly people simultaneously in indoor environments. Applications of the system include in particular service robots for social events.