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Abstract

The process of using computer vision for multiple-objects tracking is incredibly complex. Thus, simulated data was created to mimic the complexities of more realistic data. These test cases would isolate a few of the inaccuracies of real data and allow the researchers to determine what factor of said data is the most detrimental to the object-tracking process. Due to the large quantity of factors at play, Cotter’s method was used to analyze the significance of each factor. The number of detections and the number of centroids were the main dependent results that were utilized to analyze the data. The overall results show that the most significant detriment of successful object tracking is a lack of data in quantity. Additionally, the results show that if particles move too fast for proper imaging, the resultant data is inaccurate. In the future other methods of particle detection should be explored, as currently Kalman filtering is not a viable option for multiphase energetic experiments.

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