This is a tutorial on video modeling organized by Amazon AWS. Our target audience includes students, researchers and engineers, who are interested in learning the recent advances in video modeling, performing research and applying them to real-world problems.

In this tutorial, we will have six technical sessions. We first briefly introduce the problem of human activity understanding in videos, including its input data, common tasks, popular models, and the open challenges. Following it, we dive deep into the technical details, and review recent video modeling methods in a chronological manner. We also introduce GluonCV video model zoo, which has coverage for popular video models and datasets with extensive tutorials. In order to train deep video models efficiently, we introduce an efficient video reader, Decord. Decord provides easy-to-use python interface for video slicing and high efficiency over existing video readers like OpenCV and PyAV. Then, we transit from modeling to deployment, and introduce the best practices we use to deploy video models to production ready devices, such as Jetson Nano/Xavier. In the end, we walk through the diverse set of video research being done at AWS, including tracking, pose estimation, activity classification and action detection.