In this work, a secure wireless sensor network (WSN) for the surveillance, monitoring and protection of critical infrastructures was developed. To guarantee the security of the system, the main focus was the implementation of a unique security concept, which includes both security on the communication level, as well as mechanisms that ensure the functional safety during its operation. While there are many theoretical approaches in various subdomains of WSNs-like network structures, communication protocols and security concepts-the construction, implementation and real-life application of these devices is still rare. This work deals with these aforementioned aspects, including all phases from concept-generation to operation of a secure wireless sensor network. While the key focus of this paper lies on the security and safety features of the WSN, the detection, localization and classification capabilities resulting from the interaction of the nodes' different sensor types are also described.

Time-of-flight (Tof) imaging based on the photonic mixer device (PMD) or similar ToF imaging solutions has been limited to short distances in the past, due to limited lighting devices and low sensitivity of ToF imaging chips. Long-range distance measurements are typically the domain of laser scanning systems. In this paper, PMD based medium-and long-range lighting devices working together with a 2-D/3-D camera are presented and several measurement results are discussed. The proposed imaging systems suffer from two systematic limitations in addition to problems due to wind and insufficient lighting: a low lateral resolution of the depth imaging chip and ambiguities in the distance measurements. In order to provide a robust and flexible system, we introduce algorithms to obtain unambiguous depth values (phase unwrapping) and to perform a joint motion compensation and super-resolution. Several experiments were conducted in order to evaluate the components of the multimodal imaging system.

Imaging technology is an important research area and it is widely utilized in a growing number of disciplines ranging from gaming, robotics and automation to medicine. In the last decade 3D imaging became popular mainly driven by the introduction of novel 3D cameras and measuring devices. These cameras are usually limited to indoor scenes with relatively low distances. Benjamin Langmann introduces medium and long-range 2D/3D cameras to overcome these limitations. He reports measurement results for these devices and studies their characteristic behavior. In order to facilitate the application of these cameras, common algorithms are adapted to the 2D/3D data and new approaches for standard computer vision tasks are introduced.

In this paper a secure wireless sensor network (WSN) developed within the MOVEDETECT project is presented. The goal of the project was to design, implement and demonstrate a secure WSN for the protection of critical infrastructure. In order to provide a reliable service, the system must detect any kind of tampering with the sensor nodes, prevent eavesdropping and manipulation of the communication as well as detect, track and classify intruders in the protected region. Therefore based on previous experiences, a real-world WSN was developed, which addresses practical issues like water proofing, energy consumption, sensor deployment and visualization of the WSN state, but also provides a unique security concept, a interesting combination of sensors and sophisticated sensor data processing and analysis. The system was evaluated by examining firstly the sensors and the sensor processing algorithms and then conducting realistic field test.

In recent years, depth cameras gained increasing acceptance in the areas of robotics and autonomous systems. However, on mobile platforms depth measurements with continuous wave amplitude modulation Time-of-Flight cameras suffer from motion artifacts, since multiple acquisitions are required in order to compute one depth map (resulting in longer effective exposure times). Some lenses of different manufacturers include image stabilizers, but they are only able to compensate for small image shifts. Moreover, when performing a phase unwrapping based on the acquisition of multiple depth maps with different modulation frequencies, the motion artifacts are significantly more severe. In this paper, a method to compensate camera motions during the acquisition of a single depth map as well as for multiple depth maps is presented. Image shifts are estimated firstly and after normalization the individual phase images are shifted accordingly. The proposed approach is evaluated on different scenes and it is able to facilitate ToF imaging on mobile platforms.

Range imaging based on the Time-of-Flight (ToF) principle evolved largely in recent years. Especially, the lateral resolution, the ability to operate outdoors with sunlight and the sensitivity have been improved. Nevertheless, the acceptance of depth cameras for machine vision in the industry environment is still rather limited. The major shortcoming of ToF depth cameras compared to laser range scanners is their measuring accuracy, which is not sufficient for several applications. In this paper, we firstly introduce several state of the art depth cameras briefly and demonstrate their capabilities. Afterwards, we explore possibilities to increase the radial resolution and the accuracy of ToF depth cameras based on the Photonic Mixer Device (PMD). In general, the usage of higher modulation frequencies promises higher depth resolution but yields on the other hand higher noise levels. Moreover, the accuracy is limited by systematic errors and the measurement are affected by random noise and we show how to minimize and compensate them in industry environments.

An approach for auto-calibration and validation of depth measurements gained from range cameras is introduced. Firstly, the geometry of the scene is reconstructed and its surface normals are computed. These normal vectors are segmented in 3D with the Mean-Shift algorithm and large planes like walls or the ground plane are recovered. The 3D reconstruction of the scene geometry is then utilized in a novel approach to derive principal camera parameters for range or depth cameras. It operates based on a single range image alone and does not require special equipment such as markers or a checkerboard and no specific measurement procedures as are necessary for previous methods. The fact that wrong camera parameters deform the geometry of the objects in the scene is utilized to infer the constant depth error (the phase offset for continuous wave ToF cameras) as well as the focal length. The proposed method is applied to ToF cameras which are based on the Photonic Mixer Device to measure the depth of objects in the scene. Its capabilities as well as its current and systematic limitations are addressed and demonstrated.

A versatile multi-image segmentation framework for 2D/3D or multi-modal segmentation is introduced in this paper with possible application in a wide range of machine vision problems. The framework performs a joint segmentation and super-resolution to account for images of unequal resolutions gained from different imaging sensors. This allows to combine high resolution details of one modality with the distinctiveness of another modality. A set of measures is introduced to weight measurements according to their expected reliability and it is utilized in the segmentation as well as the super-resolution. The approach is demonstrated with different experimental setups and the effect of additional modalities as well as of the parameters of the framework are shown.

How good are cheap depth cameras, namely the Microsoft Kinect, compared to state of the art Time-of- Flight depth cameras? In this paper several depth cameras of different types were put to the test on a variety of tasks in order to judge their respective performance and to find out their weaknesses. We will concentrate on the common area of applications for which both types are specified, i.e. near field indoor scenes. The characteristics and limitations of the different technologies as well as the concrete hardware implementations are discussed and evaluated with a set of experimental setups. Especially, the noise level and the axial and angular resolutions are compared. Additionally, refined formulas to generate depth values based on the raw measurements of the Kinect are presented.

We present the principal aspects and the concept of a monocular combination of a scanning 3D time-of-flight sensor with a large-scale conventional 2D image sensor. While the 2D sensor profits from the whole field of view of an F-Mount photo film format lens, the smaller-sized 3D sensor is mounted onto a highly precise XY linear move stage. Thus, by the means of macro-scanning, the 3D sensor can be moved to interesting parts of the scene so as to provide a second modality e.g. for classification purposes, or, in the same setup, micro-scanning can be applied to enhance the lateral 3D resolution. A test setup was realized to verify the performance.

In this paper a tracking approach designed to utilize multiple cameras with optional depth information, e.g., ToF cameras, structured light cameras and stereo or multi camera setups, is discussed which combines photometric tracking with volumetric tracking. It is able to work with any number and type of cameras. In order to achieve this objective the tracked object is modeled in 3D with an ellipsoid. To make use of the depth information the density of the observed space is modeled with a set of Gaussian kernels for each line of sight. A proposed target configuration is then evaluated by projecting each observed color image onto the ellipsoid and comparing this projection to the expected appearance. Additionally, the density of the space occupied by the ellipsoid is estimated and compared to the expected density. It is demonstrated that by utilizing the depth information in this way ambiguities due to color similarities can be overcome reliably.

For a few years cameras have been available which are able to provide depth values gained from a PMD chip through the Time-of-Flight principle. Recently, cameras combining a normal color chip with such a PMD chip in a monocular setup have been developed. One drawback of these 2D/3D cameras is that the resolution of the depth images is much lower than those of the color images due to the limited resolution of current PMD chips. This holds true whenever PMD cameras and normal cameras are used together. However, for certain applications high resolution depth images are desirable. The color images can be utilized to generate high resolution depth images which are closer to the ground truth than the depth images produced with common scaling methods. A widely spread method to fuse the color and depth images is cross bilateral filtering and this rather general method is adopted in several approaches. In this paper different bilateral filtering strategies are compared in theory as well as in practice and especially the iterative application is addressed. Similar approaches based on a cost volume or on Markov Random Fields are addressed additionally.

Background subtraction is a common first step in the field of video processing and it is used to reduce the effective image size in subsequent processing steps by segmenting the mostly static background from the moving or changing foreground. In this paper previous approaches towards background modeling are extended to handle videos accompanied by information gained from a novel 2D/3D camera. This camera contains a color and a PMD chip which operates on the Time-of-Flight operating principle. The background is estimated using the widely spread Gaussian mixture model in color as well as in depth and amplitude modulation. A new matching function is presented that allows for better treatment of shadows and noise and reduces block artifacts. Problems and limitations to overcome the problem of fusing high resolution color information with low resolution depth data are addressed and the approach is tested with different parameters on several scenes and the results are compared to common and widely accepted methods.