Abstract
This paper presents a computer vision system for tracking and predicting flying balls in 3-D from a stereo-camera. It pursues a “textbook-style” approach with a robust circle detector and probabilistic models for ball motion and circle detection handled by state-of-the-art estimation algorithms. In particular we use a Multiple-Hypotheses Tracker (MHT) with an Unscented Kalman Filter (UKF) for each track, handling multiple flying balls, missing and false detections and track initiation and termination.
The system also performs auto-calibration estimating physical parameters (ball radius, gravity relative to camera, air drag) simply from observing some flying balls. This reduces the setup time in a new environment.
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Birbach, O., Frese, U. (2009). A Multiple Hypothesis Approach for a Ball Tracking System. In: Fritz, M., Schiele, B., Piater, J.H. (eds) Computer Vision Systems. ICVS 2009. Lecture Notes in Computer Science, vol 5815. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04667-4_44
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DOI: https://doi.org/10.1007/978-3-642-04667-4_44
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