Deep Domain Adaptation and Fusion for Person Recognition in the Wild

用于野外人员识别的深度域适应和融合

• 批准号: 543663-2019
• 负责人: Granger, Eric
• 金额: $ 5.1万
• 依托单位: École de technologie supérieure
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=699679
• 关键词: Deep; Domain; Adaptation; Fusion; Person

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.

Nuvoola Inc.是一家加拿大公司，提供基于人工智能的云托管服务和解决方案。它寻求开发用于跟踪出现在现实世界视频监控环境中的个人位置的软件。特别是，Nuvoola寻求开发准确的个人重新识别(Re-ID)系统，该系统允许识别随着时间推移出现的同一个感兴趣的人，并跨多个分布式摄像头。由于拍摄条件的变化，例如摄像机的视点、姿势、光照、比例、运动模糊、背景、遮挡，重新ID是一个具有挑战性的问题。最先进的Re-ID系统使用深度暹罗网络进行匹配，但这些深度模型需要对大量不同的标签数据进行监督训练，以实现高水平的准确性和稳健性。然而，对于现实世界的应用程序，如果开发和操作环境的捕获条件之间存在相当大的差异，重新ID准确性可能会下降。该项目的第一个目标是通过学习领域不变的表示来研究和开发适合于实际应用中的深连体网络的精确域适应(DA)的深度学习体系结构。文献中的一些技术与该项目相关，但在真实世界的Re-ID视频上提供的通用性和改进有限，其中域转移是显著的。第二个目标是研究和开发专门的深度学习模型，用于信息融合，特别是多模式、时空和基于上下文的融合。该项目涉及ETS和Nuvoola Inc.的跨学科团队，将加强思想和资源的交流，并建立长期的合作联系。这个项目的重点是从大数据集和大多数未标记的数据集设计用于视觉识别应用的深度学习模型，该项目将专注于最先进的研究。这一研究项目的重大成果将在高质量的科学期刊和会议上传播，并在Nuvoola视频分析应用程序中加以利用。它还允许培训高素质的人员，以应对战略利益领域当前和未来的挑战。