Bellotto, Nicola and Hu, Huosheng (2007) Multisensor data fusion for joint people tracking and identification with a service robot. In: IEEE Int. Conf. on Robotics and Biomimetics (ROBIO), Sanya, China.

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Abstract

Tracking and recognizing people are essential skills modern service robots have to be provided with. The two tasks are generally performed independently, using ad-hoc solutions that first estimate the location of humans and then proceed with their identification. The solution presented in this paper, instead, is a general framework for tracking and recognizing people simultaneously with a mobile robot, where the estimates of the human location and identity are fused using probabilistic techniques. Our approach takes inspiration from recent implementations of joint tracking and classification, where the considered targets are mainly vehicles and aircrafts in military and civilian applications. We illustrate how people can be robustly tracked and recognized with a service robot using an improved histogram-based detection and multisensor data fusion. Some experiments in real challenging scenarios show the good performance of our solution.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	Tracking and recognizing people are essential skills modern service robots have to be provided with. The two tasks are generally performed independently, using ad-hoc solutions that first estimate the location of humans and then proceed with their identification. The solution presented in this paper, instead, is a general framework for tracking and recognizing people simultaneously with a mobile robot, where the estimates of the human location and identity are fused using probabilistic techniques. Our approach takes inspiration from recent implementations of joint tracking and classification, where the considered targets are mainly vehicles and aircrafts in military and civilian applications. We illustrate how people can be robustly tracked and recognized with a service robot using an improved histogram-based detection and multisensor data fusion. Some experiments in real challenging scenarios show the good performance of our solution.
Keywords:	People Tracking and Identification, Histogram-based Detection, Multisensor Data Fusion, Service Robotics
Subjects:	H Engineering > H671 Robotics
Divisions:	College of Sciences > Faculty of Science > Lincoln School of Computer Science
Depositing User:	Nicola Bellotto
Date Deposited:	12 Dec 2009 17:23
Last Modified:	13 Mar 2013 08:34
URI:	http://eprints.lincoln.ac.uk/id/eprint/2099

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