Multi-Modality-Enriched Video: Potential, Strategies and Applications

视觉传感器无处不在，并且已经找到了它们进入许多不同应用的方式。它们以令人难以置信的速度捕获数据，并与其他传感器集成到单个设备中。例如，典型的智能电话具有相机（视觉传感器）、GPS传感器、加速度计、陀螺仪和磁力计。这些整合的传感器生成多模态丰富的视频信号：视频信号以及一个或多个1-D或2-D非视频信号。非视频信号可以提供用于视频信号处理的附加上下文或直接提供辅助信息以促进视频信号处理和通信。例如，运动传感器可以提供对运动估计以及因此对视频压缩有用的运动数据。此外，多模态丰富的视频使我们能够开发新的功能并创造新的用户体验。丰富的非视频信号具有巨大的潜力，可以改善我们基于视频进行通信和决策的方式。然而，这种潜力的实现取决于用于管理、分析和从这些多模态数据集提取有用信息的技术的进步。我们利用丰富的视觉传感器获得的不同信号模态之间的相互依赖性是相当直观的。目前，还没有确定在特定的视频处理任务中使用一种以上的信号模态是否有任何好处的基本原理（例如视频压缩、检测、跟踪）以及使用哪一种以及如何使用它。在这项发现资助研究中，将进行一项新的调查，以便系统地利用不同信号模态之间的相互依赖性进行视频压缩，后处理、检测和分类。将采用基于张量的框架，该框架将表征感测模态之间的相互作用。该研究有望为多模态丰富视频的系统分析和处理提供新的方法。该研究将导致上级视频通信和更好的基于视频的跟踪，识别和分类。从这项研究中产生的新知识将在教育，医疗保健，制造业和汽车工业等对加拿大非常重要的领域中找到新技术和产品。这一领域的加拿大公司将受益于新的知识和参与研究的高素质人员。

Visual sensors are ubiquitous and have found their ways into many different applications. They capturing data at an incredible pace and are being integrated with other sensors into single devices. For example a typical smartphone has a camera (visual sensor), a GPS sensor, an accelerometer, a gyroscope, and a magnetometer. These consolidated sensors generate multi-modality-enriched video signal: video signal as well as one or multiple 1-D or 2-D non-video signals. The non-video signals can provide additional context for video signal processing or directly provide side information to facilitate video signal processing and communication. For example a motion sensor can provide motion data useful for motion estimation and thus video compression. Moreover, multi-modality enriched video enables us to develop new functionalities and create new user experiences. The enriching non-video signals have a tremendous potential for improving the way we communicate and make decision based on video. However, realization of this potential hinges upon advancement in techniques for managing, analyzing, and extracting useful information from these multi-modality data sets. Our utilization of interdependency between different signal modality acquired by enriched visual sensors is rather intuitive. Currently, there is no established rationale for determining if there are any benefits to use more than one signal modality in a particular video processing task (e.g. video compression, detection, tracking) and which one to use and how to use it. In this Discovery Grant research, a new line of inquiry will be pursued in order to systematically utilize the inter dependencies between different signal modalities for video compression, post processing, detection and classification. A tensor based framework will be adopted that will characterize the interaction between sensing modalities. The proposed research is expected to yield new approaches for systematic analysis and processing of multi-modality enriched video. The research will lead to superior video communication and better video-based tracking, recognition and classifications. The new knowledge arising from this research will find its way into new technologies and products in areas of great importance to Canada such as education, healthcare, manufacturing and auto industry. Canadian companies in this area will benefit from the new knowledge and also the highly qualified personnel that will participate in the research.