Advancing Object Detection and Tracking Frontiers in Intelligent Vision-Based Applications

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Object detection and tracking (ODT) is considered a cornerstone in most intelligent vision-based applications. We define object detection as the broad research area that identifies the presence of objects of certain classes (the well-known Classification task) and localizes their position. Object tracking identifies the trajectories of the same objects over time in a video or a sequence of images. The intelligent vision-based applications market is expected to grow from $12.2 billion in 2021 to $20.5 billion in 2027. Many domains rely on these intelligent vision-based applications, including drone vision, intelligent video surveillance, autonomous driving, medical and health applications, and security. Recently, state-of-the-art ODT models have achieved great success using supervised learning with the aid of massive labelled training datasets, even surpassing human-level performance in some cases (e.g. classification on ImageNet). However, these models are still limited in terms of the scope of the problems they can solve, and they need to "increase their out-of-domain robustness." In other words, they perform well on the specialized tasks in the specific domains they have been trained on (in-distribution), but when a domain shift happens, they "are often brittle outside of the narrow domain or scope they have been trained on," as noted in a recent July 2021 article by the Turing Award winners Bengio, LeCun, and Hinton. Adapting to domain shifts is natural to humans but is still a massive challenge for intelligent vision-based applications. A recent tragic real-life example is a March 2018 fatal collision resulting from the vision system of a self-driving car miss-classifying a pedestrian for whole six seconds during the night as different classes of objects moving at different speeds in different frames (unknown object, then as a vehicle, and finally as a bicycle). Hence, it is critical for ODT performance to be both highly accurate and consistent under different scenarios and shifts. In doing so, this will help us to progress towards developing reliable, intelligent systems that can learn and adapt much like humans. The long-term objective of this research program is to advance intelligent vision-based applications through developing and creating the next-generation object detection and tracking models. More specifically, in the short term, over the next five years, I will address the following two themes: 1) developing new techniques for domain generalization in object detection and 2) developing new techniques for robust cross-domain appearance models in object tracking. This research program will provide training to at least 13 HQP, helping them build strong backgrounds in advanced topics in image processing, computer vision, deep learning, optimization, and computational complexity analysis.