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A gaze detecting tracker

Zwinkels, T.J.W. (2009) A gaze detecting tracker. Master's Thesis / Essay, Artificial Intelligence.

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Abstract

In order for robots to be accepted as members of our lives, it’s not enough for them to be just aware of unmoving, static parts of their environments. For desirable social behavior, they need to be aware of the people in the near environment, and the attitude of these people towards the robot. Aim of this research is to implement a multi-person tracking and gaze-detection system that can be used as a basis for human interaction interest detection. This system has been designed to be deployed on the Carnegie Mellon University Snackbot, which has recently been completed. Proposed is a two-staged system. The first stage performs person tracking. This system allows to answer the question ‘Where are persons in the robots environments?’. Sensor data from a SICK laser-rangefinder mounted at leg height is used by a leg detector to detect possible person positions. Leg-detections are fed to a Kalman filter based tracker. Several leg detectors and tracking strategies have been compared. The second stage performs gaze detection. A novel approach towards the problem of gaze detection is employed; Instead of building a highly specific and complex multi-layered system, a relatively simple, elegant simple features based system is used to perform binary gaze classification, answering the question: ‘is someone looking at the robot?’. Indications of a person looking at the robot, could eventually be used as an indication that the person wants or expects something from the robot. This system has been implemented on a MobileRobots inc. Peoplebot robot platform. We are comparing performance at differing conditions, angles and distances for k-nearest neighbor, perceptron, and naive Bayes machine learners. Experimentation shows good performance for the person tracker, tracking up to 90% of multiple-person movements correctly. In a difficult environment at a range of up to 3.6 meters, the gaze-detector has a classification accuracy of 72% per frame. Results could probably be improved by combining results from multiple frames.

Item Type: Thesis (Master's Thesis / Essay)
Degree programme: Artificial Intelligence
Thesis type: Master's Thesis / Essay
Language: English
Date Deposited: 15 Feb 2018 07:28
Last Modified: 15 Feb 2018 07:28
URI: http://fse.studenttheses.ub.rug.nl/id/eprint/8527

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