Abstract
Smart cameras integrate sensing, processing, and communication on embedded platforms and serve as key components at the sensory edge of many Internet of Things (IoT) and cyber-physical systems (CPS) applications. Although impressive technological progress has been achieved over the last decade, smart cameras still face resource limitations, particularly when used for advanced networked data processing. This position paper briefly summarizes trends in pervasive smart cameras, presents general strategies for improving resource efficiency, and discusses the resource challenges of person re-identification; a prototypical example of surveillance applications for smart cameras based on advanced machine learning techniques. Lastly, we present our concept for resource-efficient decentralized person re-identification in smart camera networks as a work in progress.