TITLE: Object-level Matching In Large Video Streams Using Deep Features
PRESENTER: George Strauch
ADVISOR: Dr. Jelena Tesic
DATE/TIME: Thursday, December 2nd @5:00 p.m.
LOCATION: Comal 310
Deep feature extraction using convolutional neural networks (CNNs) provides an effective method of similarity search for objects in petabytes of video feeds. We have implemented and evaluated a pipeline to extract features from multiple datasets using 2 state-of-art deep architectures and modeling approaches (out-of-box model and specifically trained model for the domain. We have evaluated the performance of the similarity search in deep feature vector space on large computer vision benchmarks: DOTA and VisDrone; and implemented a feature extraction, indexing, and search processing pipeline that can be easily extended for different deep frameworks, features (object, track, re-identification), and indexing methods.
Deadline: Dec. 3, 2021, midnight