Graduate Thesis Or Dissertation


Probabilistic and Statistical Methods for Target Tracking Public Deposited
  • Accurate and efficient tracking of objects through frames of a video is important in a wide range of areas including surveillance, military, and medical imaging applications, as well the understanding of social interactions of biological populations such as swarming insects. In this thesis, we review some of the most popular deterministic template matching algorithms for tracking, including the seminal Lucas-Kanade algorithm. We also review a Monte Carlo method and introduce a simple probabilistic algorithm for parameter learning. Additionally, we offer some improvements for existing algorithms, including a template stabilizer, formed from a principle components analysis, and an additional stopping rule for iterative attempts at matching that improves the speed of existing algorithms and in some cases results in better accuracy. Existing and new methods are compared on simulated images and on real video. In several cases, R code is provided.
Date Issued
  • 2015
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Last Modified
  • 2019-11-17
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