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Out-of-the-box code and models for CMU's object detection and tracking system for multi-camera surveillance videos. Speed optimized Faster-RCNN model. Tensorflow based. Also supports EfficientDet. WACVW'20
Activity Detection Using Unsupervised Learning Algorithms: DBSCAN, K-Means & Spectral Clustering to identify and label different activities in a dataset
Activity and Sequence Detection Evaluation Metrics: A package to evaluate activity detection results, including the sequence of events given multiple activity types.
2-layer neural network that predicts exercise activity through IMU sensor data. For the graduate course "Introduction to Optimization and Machine Learning" at SJSU. Project partners, Antonio Cervantes and Christian Pedrigal.