College of Engineering
Computer Engineering Department
BS in Computer Engineering
With the increased accessibility to powerful GPUs, ability to develop machine learning algorithms has increased significantly. Coupled with open source deep learning frameworks, average users are now able to experiment with convolutional neural networks (CNNs) to solve novel problems. This project sought to train a CNN capable of classifying between various locations within a building. A single continuous video was taken while standing at each desired location so that every class in the neural network was represented by a single video. Each location was given a number to be used for classification and the video was subsequently titled locX. These videos were converted to frames to train several well known CNNs using fine-tuning. Once the CNNs were trained, their performance on test sets of photos were observed.