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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
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.
Activity and Sequence Detection Evaluation Metrics: A package to evaluate activity detection results, including the sequence of events given multiple activity types.