Modern day computer vision applications are frequently implemented using machine learning approaches. While these implementations can perform very well, the performance is heavily dependent on sufficient and accurate training data. Due to a lack of adequate training data, the Arizona Autonomous Vehicles Club (AZA) decided to implement the generalized hough transform to detect shapes in a live video feed from an unmanned aerial system (UAS). The hough transform is computationally intensive and since real-time performance is required, a serial approach may not have the execution speed necessary for the application. Image processing techniques include matrix multiplication and convolution operations which are highly parallelizable. Therefore, the algorithm was parallelized and implemented on a graphics processing unit (GPU). Performance profiling was done on both machine learning and traditional approaches where execution time and accuracy were compared.International Foundation for TelemeteringProceedings from the International Telemetering Conference are made available by the International Foundation for Telemetering and the University of Arizona Libraries. Visit https://telemetry.org/contact-us/ if you have questions about items in this collection
