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

点击翻译按钮获取中文摘要

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.