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Camera systems are used in many applications including video surveillance for crime prevention and investigation, traffic monitoring on highways or building monitoring and automation. With the shift from analog towards digital systems, the capabilities of cameras are constantly increasing. Today's smart camera systems come with considerable computing power, large memory, and wired or wireless communication interfaces. With onboard image processing and analysis capabilities, cameras not only open new possibilities but also raise new challenges. Often overlooked are potential security issues of the camera system. The increasing amount of software running on the cameras turns them into attractive targets for attackers. Therefore, the protection of camera devices and delivered data is of critical importance. In this work we present an embedded camera prototype that uses Trusted Computing to provide security guarantees for streamed videos. With a hardware-based security solution, we ensure integrity, authenticity, and confidentiality of videos. Furthermore, we incorporate image timestamping, detection of platform reboots, and reporting of the system status. This work is not limited to theoretical considerations but also describes the implementation of a prototype system. Extensive evaluation results illustrate the practical feasibility of the approach.

Technology advances and a growing field of applications have been a constant driving factor for embedded systems over the past years. However, the increasing complexity of embedded systems and the emerging trend to interconnections between them lead to new challenges. Intelligent solutions are necessary to solve these challenges and to provide reliable and secure systems to the customer under a strict time and financial budget. Typically, intelligent solutions often come up with an orthogonal and interdisciplinary approach in contrast to traditional ways of engineering solutions. Many possible intelligent methods for embedded systems are biologically inspired, such as neural networks and genetic algorithms. Multiagent systems are also prospective for an application for nontime critical services of embedded systems. Another field is soft computing which allows a sophisticated modeling and processing of imprecise (sensory) data. Thus, as expected, we received a variety of papers with interesting solutions within the topic of the special issue. We hope that this special issue will be as inspiring as it was for the editorial team.

There is currently a strong trend towards the deployment of advanced computer vision methods on embedded systems. This deployment is very challenging since embedded platforms often provide limited resources such as computing performance, memory, and power. In this paper we present a multicamera tracking method on distributed, embedded smart cameras. Smart cameras combine video sensing, processing, and communication on a single embedded device which is equipped with a multiprocessor computation and communication infrastructure. Our multicamera tracking approach focuses on a fully decentralized handover procedure between adjacent cameras. The basic idea is to initiate a single tracking instance in the multicamera system for each object of interest. The tracker follows the supervised object over the camera network, migrating to the camera which observes the object. Thus, no central coordination is required resulting in an autonomous and scalable tracking approach. We have fully implemented this novel multicamera tracking approach on our embedded smart cameras. Tracking is achieved by the well-known CamShift algorithm; the handover procedure is realized using a mobile agent system available on the smart camera network. Our approach has been successfully evaluated on tracking persons at our campus.