Video Database System

Over the last decade, deep learning models have radically changed the world of computer vision. They are accurate on a variety of tasks ranging from image classification to emotion detection. However, there are two challenges that prevent a lot of users from benefiting from these models.

Usability and Application Maintainability

To use a vision model, the user must do a lot of imperative programming across low-level libraries, like OpenCV and PyTorch. This is a tedious process that often leads to a complex program or Jupyter Notebook that glues together these libraries to accomplish the given task. This programming complexity prevents a lot of people who are experts in other domains from benefiting from these models.

Historically, database systems have been successful because the query language is simple enough in its basic structure that users without prior experience are able to learn a usable subset of the language on their first sitting. EVA supports a declarative SQL-like query language, called EVAQL, that is designed to make it easier for users to leverage these models. With this query language, the user may compose multiple models in a single query to accomplish complicated tasks with minimal programming.

Here is a illustrative query that examines the emotions of actors in a movie by leveraging multiple deep learning models that take care of detecting faces and analyzing the emotions of the detected bounding boxes:

SELECT id, bbox, EmotionDetector(Crop(data, bbox))
FROM Interstellar
     JOIN LATERAL UNNEST(FaceDetector(data)) AS Face(bbox, conf)
WHERE id < 15;

By using a declarative language, the complexity of the program or Jupyter Notebook is significantly reduced. This in turn leads to more maintainable code that allows users to build on top of each other’s queries.

GPU Cost and Human Time

From a cost standpoint, it is very expensive to run these deep learning models on large image or video datasets. For example, the state-of-the-art object detection model takes multiple GPU-decades to process just a year’s worth of videos from a single traffic monitoring camera. Besides the money spent on hardware, this also increases the time that the user spends waiting for the model inference process to finish.

EVA automatically optimizes the queries to reduce inference cost and query execution time using its Cascades-style query optimizer. EVA’s optimizer is tailored for video analytics. The Cascades-style extensible query optimization framework has worked very well for several decades in SQL database systems. Query optimization is one of the signature components of database systems — the bridge that connects the declarative query language to efficient execution.