Real-Time Human Pose Estimation: A Case Study in Algorithm Design for Smart Camera Networks

Abstract

Monitoring human activities finds novel applications in smart environment settings. Examples include immersive multimedia and virtual reality, smart buildings and occupancy-based services, assisted living and patient monitoring, and interactive classrooms and teleconferencing. A network of cameras can enable detection and interpretation of human events by utilizing multiple views and collaborative processing. Distributed processing of acquired videos at the source camera facilitates operation of scalable vision networks by avoiding transfer of raw images. This allows for efficient collaboration between the cameras under the communication and latency constraints, as well as being motivated by aiming to preserve the privacy of the network users (no image transfer out of the camera) while offering services in applications such as assisted living or virtual placement. In this paper, collaborative processing and data fusion techniques in a multicamera setting are examined in the context of human pose estimation. Multiple mechanisms for information fusion across the space (multiple views), time, and different feature levels are introduced to meet system constraints and are described through examples.