Smart Cameras as Embedded Systems
Computer
Human Body Model Acquisition and Tracking Using Voxel Data
International Journal of Computer Vision
3-D model-based tracking of humans in action: a multi-view approach
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
S3-R1: the IBM smart surveillance system-release 1
Proceedings of the 2004 ACM SIGMM workshop on Effective telepresence
Robust and Efficient Foreground Analysis for Real-Time Video Surveillance
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Human body pose detection using Bayesian spatio-temporal templates
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Distributed vision-based accident management for assisted living
ICOST'07 Proceedings of the 5th international conference on Smart homes and health telematics
Kinematic jump processes for monocular 3D human tracking
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Predicting 3d people from 2d pictures
AMDO'06 Proceedings of the 4th international conference on Articulated Motion and Deformable Objects
Journal of Ambient Intelligence and Smart Environments
Journal of Ambient Intelligence and Smart Environments
Efficient background subtraction for real-time tracking in embedded camera networks
Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems
PASU: A personal area situation understanding system using wireless camera sensor networks
Personal and Ubiquitous Computing
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While providing a variety of intriguing application opportunities, a vision sensor network poses three key challenges. High computation capacity is required for early vision functions to enable real-time performance. Wireless links limit image transmission in the network due to both bandwidth and energy concerns. Last but not least, there is a lack of established vision-based fusion mechanisms when a network of cameras is available. In this paper a distributed vision processing implementation of human pose interpretation on a wireless smart camera network is presented. The motivation for employing distributed processing is to both achieve real-time vision and provide scalability for developing more complex vision algorithms. The distributed processing operation includes two levels. One is that each smart camera processes its local vision data, achieving spatial parallelism. The other is that different functionalities of the whole line of vision processing are assigned to early vision and object-level processors, achieving functional parallelism based on the processor capabilities. Aiming for low power consumption and high image processing performance, the wireless smart camera is based on an SIMD (single-instruction multiple-data) video analysis processor, an 8051 micro-controller as the local host, and wireless communication through the IEEE 802.15.4 standard. The vision algorithm implements 3D human pose reconstruction. From the live image data from the sensor the smart camera acquires critical joints of the subject in the scene through local processing. The results obtained by multiple smart cameras are then transmitted through the wireless channel to a central PC where the 3D pose is recovered and demonstrated in a virtual reality gaming application. The system operates in real time with a 30 frames/sec rate.