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Distributed smart cameras (DSC) are an emerging technology for a broad range of important applications including smart rooms, surveillance, entertainment, tracking, and motion analysis. By having access to many views and through cooperation among the individual cameras, these DSCs have the potential to realize many more complex and challenging applications than singlecamera systems.This article focuses on the system-level software required for efficient streaming applications on single smart cameras as well as on networks of DSCs. Embedded platforms with limited resources do not provide middleware services well known on general-purpose platforms. Our software framework supports transparent intra-and interprocessor communication while keeping the memory and computation overhead very low. The software framework is based on a publisher-subscriber architecture and provides mechanisms for dynamically loading and unloading software components as well as for graceful degradation in case of software-and hardware-related faults. The software framework has been completely implemented and tested on our embedded smart cameras consisting of an ARM-based network processor and several digital signal processors. Two case studies demonstrate the feasibility of our approach.

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In next generation video surveillance systems there is a trend towards embedded solutions. Digital signal processors (DSP) are often used to provide the necessary computing power. The limited resources impose significant challenges for software development. Resource constraints must be met while facing increasing application complexity and pressing time-to-market demands. Recent advances in synthesis tools for Simulink suggest a high-level approach to algorithm implementation for embedded DSP systems. The model-based visual development process of Simulink facilitates simulation as well as synthesis of target specific code. In this work the modeling and code generation capabilities of Simulink are evaluated with respect to video analysis algorithms. Different models of a motion detection algorithm are used to synthesize code. The generated code targeted at a Texas Instruments TMS320C6416 DSP is compared to a hand-optimized reference. Experiments show that an ad hoc approach to synthesize complex image processing algorithms hardly yields optimal code for DSPs. However, several optimizations can be applied to improve performance.

In this paper, we present an approach for improving fault-tolerance and service availability in intelligent video surveillance (IVS) systems. A typical IVS system consists of various intelligent video sensors that combine image sensing with video analysis and network streaming. System monitoring and fault diagnosis followed by appropriate dynamic system reconfiguration mitigate effects of faults and therefore enhance the system's fault-tolerance. The applied monitoring and diagnosis unit (MDU) allows the detection of both node-and system-level faults. Lacking redundant hardware such reconfigurations are established by graceful degradation of the overall application. An optimizer module that performs multi-criterion optimization is used to compute a new degraded system configuration by trading off quality of service (QoS), energy consumption, and service availability. We demonstrate the functionality of our approach by an illustrative example.