Deep Domain Adaptation and Fusion for Person Recognition in the Wild

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Nuvoola Inc. is a Canadian company that provides cloud managed services and solutions based on artificial intelligence. It seeks to develop software for tracking the location of individuals appearing in real-world video surveillance environments. In particular, Nuvoola seeks to develop accurate person re-identification (Re-ID) systems that allow recognizing a same person of interest appearing over time, and across multiple distributed cameras. Re-ID is a challenging problem because of variations in capture conditions, e.g., cameras viewpoint, pose, illumination, scale, motion blur, background, occlusion. State-of-the-art systems for Re-ID employ deep Siamese networks for matching, yet these deep models require supervised training on a large and diverse amount of labeled data to achieve a high level of accuracy and robustness. For real-world applications, however, Re-ID accuracy can decline if there is a considerable divergence between the capture conditions of development and operational environments. The first objective of this project is to investigate and develop deep learning architectures that are suitable for accurate domain adaptation (DA) of deep Siamese networks in real-world applications by learning domain-invariant representations. Some techniques in literature are relevant for this project but provide limited versatility and improvements on real-world Re-ID videos, where domain shifts are significant. The second objective is to investigate and develop specialized deep learning models for information fusion, and in particular on multi-modal, spatiotemporal, and context-based fusion. This project involves a cross-disciplinary team from ETS and Nuvoola Inc., and will allow to intensify the exchange of ideas and resources, and establish long-term collaborative links. Focusing on the design of deep learning models for visual recognition applications from large and mostly unlabeled datasets, this project will focus on state-of-the-art research. Significant findings of this research project will be disseminated in high caliber scientific journals and conferences and exploited in Nuvoola video analytics applications. It also allows training of highly qualified personnel to face current and future challenges in areas of strategic interest.