"If we knew what it was we were doing, it would not be called research, would it?"
Albert Einstein
Agent-based Learning to Utilize Local Data for Anomalous Activity Recognition (Funded by the U.S. Department of Homeland Security)
We aim to detect anomalous events or suspicious activities such as assault, explosion, and shooting in surveillance videos.
We plan to improve the accuracy of decisions of human agents by reducing the manual work of monitoring of human agents.
We focus to provide better visualization to locate anomalous event and act accordingly.
House Detection from Aerial Images Using Faster RCNN
Demonstrate the effectiveness of Faster Region-based Convolutional Neural Network (Faster-RCNN) to detect buildings automatically from aerial images using Python programming language.
Key Notes:
Implementation of faster RCNN algorithm.
Data annotation using LabelMe.
Bounding-box information extraction from XML file.
Pre-trained ResNet50 for feature extraction.
Under Construction