DEEP LEARNING FOR FULL-MOTION VIDEO ANALYTICS
Prerequisites: Basic experience with CNNs and C++
Abstract: Smart cities collect huge amounts of video footage that require advanced deep learning techniques to transform data into actionable insights. The first step in more complex deep learning workflows is detecting specific types of objects. This involves identification, classification, segmentation, prediction, and recommendation. In this workshop, you’ll learn how to:
- Train and evaluate deep learning models using the TensorFlow object detection application programming interface (API)
- Explore the strategies and trade-offs involved in developing high quality neural network models to track moving objects in large-scale video datasets
- Optimize inference times using TensorRT for real-time applications
Upon completion, you’ll be able to deploy object detection and tracking networks to work on real-time, large-scale video streams.