Invariant Person Detection in RGB-D Data

Artificial Intelligence can play a key role in healthcare; however, due to patient confidentiality (HIPAA), we are unable to process this information without putting up some boundaries. This boundary comes in the form of RGB-D data; it prevents us from seeing a face or a distinguishing personal characteristic in videos. This project attempts to detect a person from any viewpoint in Stanford Health’s RGB-D data. The goal is to create a detection system that will be able to identify a person from any view point. This will allow nurses and doctors to sense problems such as if a person suddenly fell or if the person has not moved in days. An SVM with HOG descriptor as features is used as a baseline. A 6-layer CNN classifier is proposed as a better system to classify the object.