Deep Video Analysis

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Over the past decade, we have experienced an exponential growth in captured videos due to the broad availability of image capture devices (e.g., mobile phones), high capacity data storage devices, and web-related video services. Video contains a wealth of information for understanding and manipulating the surrounding world; however, in its raw form it is meaningless. Computer vision provides a means for automating the process of understanding and organizing latent information in video streams. The goals of my proposed research program are to advance our fundamental understanding of video as it relates to the surrounding world (e.g., "What geometric information of the surrounding world is recoverable from video?"), develop efficient architectures for video inference by leveraging the redundant and predictable nature of video ("What previous computations can I reuse in place of new computations?"), and develop models for predicting future raw RGB frames or semantic pixel-wise labels given past video frames ("Given what I have seen so far, what imagery comes next?"). A key theme underlying my research program is leveraging domain knowledge and constraints in the development of deep multi-layer trainable architectures. My proposed program benefits the field of computer vision research and Canada in the following ways. First, my program explores methods for making explicit pertinent information available within raw video. At its core, we are trying to uncover basic principles underpinning video understanding to guide the development of automated processing methods. Second, there are a multitude of real-world, impactful applications that can be supported by this research, including autonomous driving, video mining and organization, and security surveillance. Finally, my research program provides valuable, high quality training in the rapidly evolving areas of computer vision, machine learning, and more broadly data science. There is a growing awareness of the need for these skill-sets in both Canadian industrial research and development, and academic settings; making individuals with these skill-sets highly sought after.