Robust People Tracking and SHMM learning using SHMMs
Stephan Sehestedt, Sarath Kodagoda, Gamini Dissanayake · UTS ePRESS (University of Technology Sydney) · 2011
For effective Human Robot Interaction (HRI), it is necessary for the robot to be aware of its human peers and be able to anticipate and predict their actions. This paper explores an improved strategy for people tracking using Sampled Hidden Markov Models (SHMM) for capturing common human motion patterns. Such an SHMM contains rich information about human spatial behavior and it can be learned online during robot operation. The proposed integration of people tracking and learning offers significant improvements to the outcomes when compared to existing techniques. Real world experiments that demonstrate the viability and effectiveness of the approach are presented.