Special session on “Embedded processing in distributed camera networks”

Francois Berry, Frantz Pelissier, LASMEA CNRS, University Blaise Pascal - France

Smart cameras combine video sensing, processing, and communication on a single embedded platform. Networks of smart cameras are real-time distributed embedded systems that perform computer vision using multiple cameras. This new approach has emerged thanks to a confluence of simultaneous advances in four key disciplines: computer vision, image sensors, embedded computing, and sensor networks. Recently these visual sensor networks have gained a lot of interest in research and industry; applications include surveillance, assisted living and smart environments. This special session focuses on the “embedded aspects” of smart cameras and smart camera networks. Since these networks represent large, resource-constraint, distributed embedded systems, they may serve as challenging platforms for innovative embedded systems research. The development of computer vision components (HW and SW) in smart cameras is a particularly challenging task. The nature of embedded devices limits the computational power available to the applications. The limitations of the memory and I/O systems are often more severe since they hinder the processing of large image data. Implementing and debugging on an embedded device pose several challenges –- particularly when computation is outsourced to a FPGA and/or DSP.

The goal of this special session is to bring together researchers and practitioners working in the area of embedded image processing, smart camera, HW and SW architecture for vision. We are soliciting original contributions, which address a wide range of theoretical and practical issues related to the early stages of the embedded image processing including, but not limited to:
* embedded computer vision
* Digital Camera Image Processing
* Dedicated architecture for vision (FPGA, DSP,...)
* Analysis of computer vision problems that are specific to embedded systems.
* Analysis of embedded systems problems that are specific to computer vision.
* Embedded computer vision for robotics
* New trends in programmable processors and their computational models.
* Applications of embedded computer vision on platforms e.g. GPUs (embedded and mobile).
* Applications of embedded computer vision on mobile phones.
* Applications of embedded computer vision on reconfigurable platforms such as FPGAs.
*Applications of embedded computer vision on programmable platforms DSPs and multicore SoC such as the Cell Processor.