Melanie Schranz

Distributed object tracking based on cubature Kalman filter

In this work, we propose the cubature Kalman filter (CKF) based distributed object tracking algorithm in a visual sensor network (VSN). A VSN consists of several distributed smart cameras having the ability to process and analyze the retrieved data locally. The first objective is to optimize the tracking process within the VSN through the CKF. Under the conditions of non-linear motion and observation model, the CKF based method features a considerably better tracking accuracy than the extended Kalman filter (EKF) based method in terms of the mean square error (MSE). Although, the particle filter (PF) based method shows better performance than the CKF, it is computationally very complex. The second objective is to optimize the object tracking by aggregating the tracking results from multiple cameras. Assuming the VSN is a multi-camera network with overlapping field of views (FOVs), cameras having the same object in their FOV exchange their local estimates of the objects position and velocity. During the estimation process, each of the participating cameras aggregates the received states via a consensus algorithm. Thus, the objects real state is more accurately predicted by the resulting joint state than it would be by processing only a single cameras observation.

Multi-camera object tracking using surprisal observations in visual sensor networks

In this work, we propose a multi-camera object tracking method with surprisal observations based on the cubature information filter in visual sensor networks. In multi-camera object tracking approaches, multiple cameras observe an object and exchange the object’s local information with each other to compute the global state of the object. The information exchange among the cameras suffers from certain bandwidth and energy constraints. Thus, allowing only a desired number of cameras with the most informative observations to participate in the information exchange is an efficient way to meet the stringent requirements of bandwidth and energy. In this paper, the concept of surprisal is used to calculate the amount of information associated with the observations of each camera. Furthermore, a surprisal selection mechanism is proposed to facilitate the cameras to take independent decision on whether their observations are informative or not. If the observations are informative, the cameras calculate the local information vector and matrix based on the cubature information filter and transmit them to the fusion center. These cameras are called as surprisal cameras. The fusion center computes the global state of the object by fusing the local information from the surprisal cameras. Moreover, the proposed scheme also ensures that on average, only a desired number of cameras participate in the information exchange. The proposed method shows a significant improvement in tracking accuracy over the multi-camera object tracking with randomly selected or fixed cameras for the same number of average transmissions to the fusion center.

Demo: VSNsim - A Simulator for Control and Coordination in Visual Sensor Networks

The analysis and evaluation of concepts in the research fields of visual sensor networks (VSNs) suffer from the low number of simulation possibilities. In this paper we present a simulator, the VSNsim, dedicated for evaluating control and coordination strategies in VSNs. It is built with the game engine Unity3D and has a very user friendly handling. The algorithms locally running on the sensor nodes of the VSN can be implemented in C#, JavaScript or Boo. Due to graphical user interface and the 3D implementation, our simulator is a tool that can be intuitively applied and extended to a researchers need.

The extended vsnsim for hybrid camera systems

This paper presents the extension of the simulator VSNsim to enable the simulation of hybrid camera networks in the field of coordination and control algorithms. A hybrid camera network combines mobile and static cameras. Thus, mobile cameras in form of autonomously moving robots are added to the existing static cameras of VSNsim. Furthermore, priorities can be assigned to rooms in the simulated environment in order to focus monitoring to dedicated areas.

Towards Resource-Aware Hybrid Camera Systems

We investigate how hybrid camera systems---stationary and mobile cameras---allow to improve the observability of mobile objects and/or locations considering limited resources. This Static Camera System with Mobile robots (SCSM) allows mobile cameras to observe targets where no stationary cameras are deployed, where they fail, or their field of view (FOV) is blocked. By assigning dynamic priorities to targets, the self-coordination and control of the SCSM becomes an assignment problem. Furthermore, the coordination of the available resources, especially for the mobile part of the SCSM, is considered. We adapt a market-based approach based on dynamic clustering. The SCSM is evaluated through simulation studies with a graphical simulator for visual sensor networks (VSNs). The results show that the SCSM can achieve up to 30% higher observability of targets compared to a stationary camera system. Moreover, the resource consumption can be distributed among the cameras with the dynamic clustering protocol to not burden all cameras having the same object in their FOV.

Self-calibration and Cooperative State Estimation in a Resource-aware Visual Sensor Network

In this paper we present an algorithm, which enables distributed visual sensor networks to autonomously calibrate the network and dynamically build clusters to achieve cooperative object tracking based on state estimation. A main focus is thereby on resource-awareness and -efficiency, since we aim for low-power embedded smart camera networks. We do not require any human intervention or a-priori information about the network topology to achieve calibration and tracking. Camera nodes first estimate relative positions and orientations and then use the common coordinate system to enable cooperative state estimation. For that purpose, cameras dynamically build clusters depending on their available resources. New nodes joining the network are discovered and failing nodes do not prevent others from their tasks. Compared to other methods, our approach is not only able to handle sensor measurement errors but also faulty camera positions gathered during the network calibration process.