Efficient H.264 video coding with a working memory of objects

The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.Title from PDF of title page (University of Missouri--Columbia, viewed on December 22, 2009).Thesis advisor: Professor Zhihai (Henry) He.Includes bibliographical references.M.S. University of Missouri--Columbia 2009.Dissertations, Academic -- University of Missouri--Columbia -- Electrical engineering.Efficient spatiotemporal prediction to remove the source redundancy is critical in video coding. The newest international standard H.264 video coding introduces several advanced features, such as multiple-frame motion prediction and spatial intra prediction [1], which significantly improve the overall coding efficiency. In this work, we focus on efficient H.264 video coding for video monitoring and surveillance. The video camera, mostly stationary, watches the surveillance scene continuously, compresses the video streams which are then transmitted to a remote end for information analysis or archived in a storage device. In these types of video monitoring and surveillance scenarios, the video frame rate is often set relatively low and the activities of persons in the scene often exhibit strong patterns which might repeat at different spatiotemporal scales. In this work, we aim to develop efficient methods to exploit this type of long-term source correlation to improve the overall video compression efficiency. We propose a working memory approach for efficient temporal prediction in H.264 video coding. After video frames are encoded, objects are extracted, analyzed, and indexed in a dynamic database which acts as a working memory for the H.264 video encoder. At the same time, silhouettes are evaluated by using different compression configurations and comparing with ground truth. During the encoding process, objects with similar spatial characteristics are retrieved from the working memory and used for motion prediction of objects in the current video frame. This approach extends the multiple-frame estimation and provides a more generic framework for spatiotemporal prediction of video data. Our experimental results on indoor activity monitoring video data demonstrate that the proposed approach is able to save the coding bit rate by up to 35% with a small computational overhead

A Real-time System for In-home Activity Monitoring of Elders

In this paper, we propose a real-time system for in-home activity monitoring and functional assessment for elder care. We describe the development of the whole system which could be used to assist the independent living of elders and improve the efficiency of eldercare practice. With this system, data is collected, silhouettes extracted, features further analyzed and visualized into graphs from which eldercare professionals are able to understand massive video monitoring data within a short period of time. Our experimental results demonstrate that the proposed system is efficient in indoor elder activities monitoring and easily utilized by eldercare professionals.This work was supported in part by National Institutes of Health under grant 1R21AG026412-01A2