Computer Vision

Submission is in progress

Submission is in progress

3D human pose tracking is difficult. Recent works in the computer vision space have made major strides in estimating 3D human motion from monocular video. The core aspect of our project is the question：can motions captured from video be used in games? To answer this question, we built a VR game in Unity that simultaneously utilizes both video-distilled motions and traditional motion capture motions. This allows us to evaluate the accuracy and usability of these motions in their native 3D environment.

By learning the common structure between all LDR-to-HDR conversion tasks, our model is able to adapt it’s predictions given extra exposures of a scene. This novel approach reframes LDR-to-HDR conversion as a meta-learning problem.

This final project explores the feasibility of using a GAN based style-transfer approach to generating semantic segmentation masks for autonomous driving application. Specifically, CycleGAN and pix2pix are benchmarked against a more traditional U-Net architecture built off of Resnet-18. The project uses a subset of the image data from the ApolloScape open dataset.

Classification of detections as either running pedestrians or driving cars is achieved using only the range-doppler information from a single forward facing automotive PMCW radar system.

Millimeter-wave radar has the potential to offer a weather-agnostic sensing solution to pedestrian classification. To this end, I applied a CNN architecture to micro-doppler spectrogram data simulated through Matlab in an attempt to distinguish between a pedestrian radar motion spectrogram and a bicycle radar motion spectrogram.