Integrating multi-camera tracking into a dynamic task allocation system for smart cameras

This paper reports on the integration of multi-camera tracking into an agent-based framework, which features autonomous task allocation for smart cameras targeting traffic surveillance. Since our target platforms are distributed embedded systems with limited resources, the trackers may only be active, if the target is in the camera’s field of view. Consequently, the tracking algorithm has to migrate from camera to camera in order to follow the target, whereas the decision when and whereto the migration takes place is reached autonomously by the tracker. Consequently, no central control host is required. We further present different strategies on when to migrate a tracker, and how to determine the camera which will observe the tracked object subsequently. We have realized the tracker’s control by using heterogeneous mobile agents, which employ a state-of-the-art tracking algorithm for tracking. The tracking system has been implemented on our smart cameras (SmartCam) which are comprised of a network processor and several digital signal processors (DSPs) and provide a complex software framework

Networked Uavs As Aerial Sensor Network For Disaster Management Applications

Advances in control engineering and material science made it possible to develop small-scale unmanned aerial vehicles (UAVs) equipped with cameras and sensors. These UAVs enable us to obtain a bird\u27s eye view of the environment. Having access to an aerial view over large areas is helpful in disaster situations, where often only incomplete and inconsistent information is available to the rescue team. In such situations, airborne cameras and sensors are valuable sources of information helping us to build an overview of the environment and to assess the current situation. This paper reports on our ongoing research on deploying small-scale, battery-powered and wirelessly connected UAVs carrying cameras for disaster management applications. In this aerial sensor network several UAVs fly in formations and cooperate to achieve a certain mission. The ultimate goal is to have an aerial imaging system in which UAVs build a flight formation, fly over a disaster area such as wood fire or a large traffic accident, and deliver high-quality sensor data such as images or videos. These images and videos are communicated to the ground, fused, analyzed in real-time, and finally delivered to the user. In this paper we introduce our aerial sensor network and its application in disaster situations. We discuss challenges of such aerial sensor networks and focus on the optimal placement of sensors. We formulate the coverage problem as integer linear program (ILP) and present first evaluation results. © 2010 Springer-Verlag