Abstract
We present a multilevel system architecture for intelligent environments equipped with omnivideo arrays. In order to gain unobtrusive human awareness, real-time 3D human tracking as well as robust video-based face detection and tracking and face recognition algorithms are needed. We first propose a multiprimitive face detection and tracking loop to crop face videos as the front end of our face recognition algorithm. Both skin-tone and elliptical detections are used for robust face searching, and view-based face classification is applied to the candidates before updating the Kalman filters for face tracking. For video-based face recognition, we propose three decision rules on the facial video segments. The majority rule and discrete HMM (DHMM) rule accumulate single-frame face recognition results, while continuous density HMM (CDHMM) works directly with the PCA facial features of the video segment for accumulated maximum likelihood (ML) decision. The experiments demonstrate the robustness of the proposed face detection and tracking scheme and the three streaming face recognition schemes with 99% accuracy of the CDHMM rule. We then experiment on the system interactions with single person and group people by the integrated layers of activity awareness.We also discuss the speech-aided incremental learning of new faces.
Highlights
Intelligent environment is a very attractive and active research domain due to both the exciting research challenges and the importance and breadth of possible applications
We work toward the realization of such an intelligent environment using vision and audiosensors. To develop such a system, we propose the architecture for the networked omnivideo array (NOVA) system as shown in Figure 1 [6]
(2) The face analysis algorithms utilize the temporal continuity of faces in the videos in order to enhance the robustness to real-world situations and allow for natural human
Summary
Intelligent environment is a very attractive and active research domain due to both the exciting research challenges and the importance and breadth of possible applications. The central task of the intelligent environment research is to design systems that automatically capture and develop awareness of the events and activities taking place in these spaces through sensor networks [1–5]. The awareness may include where a person is, what the person is doing, when the event happens, and who the person is Such spaces can be indoor, outdoor, or mobile, and can be physically contiguous or otherwise. We do not require humans to adapt to the environments but would like the environments to adapt to the humans. This design guideline places some challenging requirements on the computer vision algorithms, especially for face detection and face recognition algorithms
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