Sayyed Raza

Project Description:

We have witnessed an explosion of video data over recent decades. Video accounted for 75% of internet traffic in 2017, and is expected to make up 82% in 2022, with close to 1 million minutes of video crossing the Internet per second. According to the Wall Street Journal there will be a billion cameras on the streets by 2021. However, the way of querying video streams is still primitive, and requires lots of human intervention. In scenarios such as surveillance applications, humans often have to visually inspect a large amount of video to identify persons or objects of interest. It is therefore crucial to develop systems that could support the extraction of meaningful information from videos in real time, utilizing declarative queries, in a way akin to how people are interacting with database systems today. On the other hand, recent breakthroughs in Deep Learning (DL) have made it possible to achieve highly accurate results in tasks such as image classification, object detection and object tracking, providing the building blocks to make large-scale video query processing a reality. We are building a video processing system that is capable of performing declarative queries over streaming videos, incorporating both spatial and temporal aspects. Through the project, the student will get exposed to recent advances in deep learning for object recognition/tracking, large-scale query processing, and front-end development.

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