Real Time Big Data Analytics on automated Classroom attendance application
Initial Commit Lab 3 : https://github.com/nikhitasharma/RTBigDataAnalytics_Project/wiki/Lab-3
Key frames extraction from video input, Face Detection in a class room scenario
Constructing a short video using key frames.
Lab 4 : https://github.com/nikhitasharma/RTBigDataAnalytics_Project/wiki/Lab-4
Video Processing and Implementing SIFT on use-case related to a classroom attendance scenario. Adding a bounding box for each detected face and counting the number of faces detected in a frame. Also, to recognize individual faces of students using SIFT matcher by building a model.
Lab 5 : https://github.com/nikhitasharma/RTBigDataAnalytics_Project/wiki/Lab-5
Implementing classification model (Decision Tree) by building on SparkMLLib and able to classify students correctly by training the model with student images and sample video using feature extraction. Reporting the F-measure, Precision , recall and Confusion Matrix.
Lab 6: https://github.com/nikhitasharma/RTBigDataAnalytics_Project/wiki/Lab-6
Implementation of sending data and metadata using Kafka Producer and Consumer.
Lab 7 & 8: https://github.com/nikhitasharma/RTBigDataAnalytics_Project/wiki/Lab-7-&-8
Implementing sending of features and metadata from client application to Kafka and from Kafka to Storm. Creating Storm topology, Spouts and Bolts creation and processing data through them.
Lab 9: https://github.com/nikhitasharma/RTBigDataAnalytics_Project/wiki/Lab9
Audio Classification involving audio feature extractio, sending features to Storm through Kafka, Storm topology creation and processing these features for audio classification using decision tree algorithm.