In recent years, the potential for groundbreaking work in the video understanding community has grown dramatically. Similarly to other AI fields, video data has become abundant through surveillance, social media, and even robotics. However, compared to parallel opportunities in image understanding, the video community has seen fewer breakthroughs. Why is that?
In this tutorial, we aim to both provide an introduction and organization of the many research directions in the video analysis field, as well as provide a single open framework, PyVideoResearch, for comparing and sharing video algorithms. We encourage both new and veteran video researchers to utilize the framework, and we hope this helps to unify the various silos producing independent video algorithms on their own datasets.

1. We provide a systematic analysis of the various areas within video analysis, where we identify a taxonomy of tasks and benchmarks in video research. We explore both the fundamental algorithms that have become dominant in recent years, and analyze the drawbacks and strengths of those algorithms and datasets.
2. We propose a new framework, PyVideoResearch in PyTorch, that contains a variety of state-of-the-art video analysis tools, datasets, and tasks. We provide an overview of the features, the design choices, and the ecosystem we hope to create. This makes it easy to try different combinations of different algorithms on different datasets, and ensures that all benefit from the same engineering tricks.
3. We will hear talks and discussions from prominent researchers in the video community on how they think about the current important directions and how to move forward.

Juan Carlos Niebles received an Engineering degree in Electronics from Universidad del Norte (Colombia) in 2002, an M.Sc. degree in Electrical and Computer Engineering from University of Illinois at Urbana-Champaign in 2007, and a Ph.D. degree in Electrical Engineering from Princeton University in 2011. He is a Senior Research Scientist at the Stanford AI Lab and Associate Director of Research at the Stanford-Toyota Center for AI Research since 2015. He is also an Associate Professor of Electrical and Electronic Engineering in Universidad del Norte (Colombia) since 2011. His research interests are in computer vision and machine learning, with a focus on visual recognition and understanding of human actions and activities, objects, scenes, and events. He is a recipient of a Google Faculty Research award (2015), the Microsoft Research Faculty Fellowship (2012), a Google Research award (2011) and a Fulbright Fellowship (2005).