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15 May 2020 Application of the TensorFlow object detection API to high speed videos of pyrotechnics for velocity calculations
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Abstract
Video analysis of pyrotechnics, or any event, from a high speed camera to obtain velocity data can be a tedious task, even with the help of most traditional software. The video has to be calibrated, the object of interest has to be identified, and the event of interest has to be monitored and analyzed. Even with an experienced user, one data point could take several minutes to obtain and may vary each time the same sample is analyzed. With the help of machine learning, a trained model could accomplish the same task, with identical results, in just seconds. The TensorFlow Object Detection API is an open source framework built on top of TensorFlow that makes it easy to construct, train and deploy object detection models.1 With some additional libraries, such as OpenCV, Pandas, Numpy, and Matplotlib a powerful object tracking, velocity calculating, and path visualization tool could be developed to break the monotony of software assisted, semiautomatic analysis and stride towards full autonomy, freeing up valuable engineering time and providing instant key performance attributes. For the first iteration of this application an object detection model was trained on a very small, annotated data set of pyrotechnics, additional scripts were written to extract velocity data and project a flight path, and the results were compared against the current processing technique. There was a significant decrease in processing time and a minuscule percent difference in the variation of data points.
Conference Presentation
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Marcin Rosol "Application of the TensorFlow object detection API to high speed videos of pyrotechnics for velocity calculations", Proc. SPIE 11413, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II, 114131Q (15 May 2020); https://doi.org/10.1117/12.2557526
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