Object Tracking by introducing Stochastic Filtering into Window-Matching Techniques

This paper describes the development and the application of an object tracking algorithm from a sequence of images. The algorithm is based on window-matching techniques using the sum of squared differences (SSD) as a distance-similarity measure, but adding stochastic filtering. The algorithm is then...

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Bibliographic Details
Published in2007 International Symposium on Computational Intelligence in Robotics and Automation pp. 31 - 36
Main Authors Vidal, F.B., Alcalde, V.H.C.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2007
Subjects
Biomedical measurements
Computational intelligence
Digital images
Filtering algorithms
Kalman filters
Motion detection
Robotics and automation
Stochastic processes
USA Councils
Vehicles
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Summary:This paper describes the development and the application of an object tracking algorithm from a sequence of images. The algorithm is based on window-matching techniques using the sum of squared differences (SSD) as a distance-similarity measure, but adding stochastic filtering. The algorithm is then applied for tracking a vehicle on an urban environment and for tracking the ball on a ping-pong game. It is concluded that incorporating the Kalman filtering greatly improves the tracking performance.
ISBN:1424407893
9781424407897
DOI:10.1109/CIRA.2007.382869