Deep Video Analysis

深度视频分析

• 批准号: RGPIN-2019-04623
• 负责人: Derpanis, Konstantinos
• 金额: $ 2.48万
• 依托单位: York University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749801
• 关键词: Deep; Video; Analysis

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.

在过去的十年中，由于图像捕获设备的广泛可用性，我们已经经历了捕获的视频的指数增长（例如，移动的电话）、高容量数据存储设备以及与web相关的视频服务。 视频包含了丰富的信息来理解和操纵周围的世界;然而，在其原始形式是没有意义的。计算机视觉提供了一种自动化的理解和组织视频流中潜在信息的方法。我提出的研究计划的目标是推进我们对视频的基本理解，因为它与周围的世界（例如，“从视频中可以恢复周围世界的哪些几何信息？“），通过利用视频的冗余和可预测性来开发有效的视频推理架构（“我可以重用哪些以前的计算来代替新的计算？），并开发模型来预测未来的原始RGB帧或给定过去视频帧的语义像素标签（“鉴于我目前所看到的，接下来会出现什么图像？我的研究项目的一个关键主题是在深度多层可训练架构的开发中利用领域知识和约束。我提出的计划有利于计算机视觉研究领域和加拿大在以下方面。首先，我的计划探索的方法，使明确的相关信息提供原始视频。在其核心，我们正试图揭示基本原则支撑视频理解，以指导自动化处理方法的发展。第二，有大量的现实世界，最后，我的研究计划在计算机视觉，机器学习，以及更广泛的数据科学。人们越来越意识到加拿大工业研发和学术环境对这些技能的需求;使具有这些技能的个人备受追捧。