Identifying the active speaker in a video of a distributed meeting can be very helpful for remote participants to understand the dynamics of the meeting. A straightforward application of such analysis is to stream a high resolution video of the speaker to the remote participants. In this paper, we present the challenges we met while designing a speaker detector for the Microsoft RoundTable distributed meeting device, and propose a novel boosting-based multimodal speaker detection (BMSD) algorithm. Instead of separately performing sound source localization (SSL) and multiperson detection (MPD) and subsequently fusing their individual results, the proposed algorithm fuses audio and visual information at feature level by using boosting to select features from a combined pool of both audio and visual features simultaneously. The result is a very accurate speaker detector with extremely high efficiency. In experiments that includes hundreds of real-world meetings, the proposed BMSD algorithm reduces the error rate of SSL-only approach by 24.6%, and the SSL and MPD fusion approach by 20.9%. To the best of our knowledge, this is the first real-time multimodal speaker detection algorithm that is deployed in commercial products.

We present a system for automatically extracting the region of interest (ROI) and controlling virtual cameras' control based on panoramic video. It targets applications such as classroom lectures and video conferencing. For capturing panoramic video, we use the FlyCam system that produces high resolution, wide-angle video by stitching video images from multiple stationary cameras. To generate conventional video, a region of interest can be cropped from the panoramic video. We propose methods for ROI detection, tracking, and virtual camera control that work in both the uncompressed and compressed domains. The ROI is located from motion and color information in the uncompressed domain and macroblock information in the compressed domain, and tracked using a Kalman filter. This results in virtual camera control that simulates human controlled video recording. The system has no physical camera motion and the virtual camera parameters are readily available for video indexing.

A method for video indexing and filtering based on motion activity characteristics in hierarchical levels is proposed. To extract motion activity information, an MPEG (MPEG-1/2) video is first adaptively segmented into hierarchical levels with fixed percentage of original video length based on P-frame macroblock motion information. Three motion activity characteristics - motion intensity which represents the degree of change in motion, motion intensity histogram which represents the temporal statistics of motion intensity, and spatial descriptor which represents the spatial attribute of motion, are then computed to represent different levels of video. The descriptors from different levels are used selectively in different steps of video indexing and filtering. Experimental results show the proposed method is fast and effective, and provides a powerful video indexing and filtering tool.

This paper presents a unified approach to human activity capturing and recognition. It targets applications such as a speaker walking, turning around, sitting and getting up from a chair in a classroom setting. A panoramic camera capturing system is designed for video capture. Virtual camera control outputs the region of interest (ROI) video that covers the speaker. Given an ROI sequence, the virtual camera control parameters are used for the recognition of activities like walking, and the motion parameters of each frame are used for the recognition of other activities like turning around, sitting down and getting up etc. For motion parameter based recognition, the likelihood of the motion parameters is represented using a multivariate Gaussian model. The temporal change of the likelihood is characterized using a continuous density hidden Markov model (HMM). Experimental results show that the method works well in recognizing the above mentioned human body activities.

Given panoramic video taken along a self-intersecting path, we present a method for detecting the intersection points. This allows "virtual tours" to be synthesized by splicing the panoramic video at the intersection points. Spatial intersections are detected by finding the best-matching panoramic images from a number of nearby candidates. Each panoramic image is segmented into horizontal strips. Each strip is averaged in the vertical direction. The Fourier coefficients of the resulting 1-D data capture the rotation-invariant horizontal texture of each panoramic image. The distance between two panoramic images is calculated as the sum of the distances between their strip texture pairs at the same row positions. The intersection is chosen as the two candidate panoramic images that have the minimum distance.

A novel method for region of interest tracking and recording video is presented. The proposed method is based on the FlyCam system (Foote et al., 2000), which produces high-resolution and wide-angle video sequences by stitching the video frames from multiple stationary cameras. The method integrates tracking and recording processes, and targets applications such as classroom lectures and videoconferencing. First, the region of interest (which typically covers the speaker) is tracked using a Kalman filter. Then, the Kalman filter estimation results are used for virtual camera control and to record the video. The system has no physical camera motion and the virtual camera parameters are readily available for video indexing. The proposed system has been implemented for real-time recording of lectures and presentations.

A system for capturing panoramic video and a novel method for corresponding compressed domain virtual camera control is presented. It targets applications such as classroom lectures and video conferencing. The proposed method is based on the FlyCam panoramic video system that is designed to produce high resolution and wide-angle video sequences by stitching the video pictures from multiple stationary cameras. The panoramic video sequence is compressed into an MPEG-2 stream for delivery. The proposed method integrates region of Interest (ROI) detection, tracking, and virtual camera control, and works on compressed domain information only. It first detects the ROI in the P (predictive coded) picture using only the macroblock type information, It then up-samples this detection result to obtain the ROI of the whole video stream. The ROI is tracked using a Kalman filter. The Kalman filter estimation results are used for virtual camera control that simulates human controlled video recording. The system has no physical camera motion and the virtual camera parameters are readily available for video indexing. The proposed system has been implemented for real time processing.