Persistent multiple object tracking in Wide Area Motion Imagery (WAMI) is fundamental for a wide range of applications, e.g. surveillance of borders. Though impressive tracking results have been achieved by combining appearance based and motion based object detection as input for modern tracking-by-detection methods, the number of identity-switches (ID-switches) in case of many slow or stopped vehicles is still high. Instead of extracting features from the appearance based object detection model for data association between object detections and existing tracks, we propose to use visual appearance descriptors learned from re-identification tasks. For this purpose, we employed the recent re-identification model OSNet and created an aerial vehicle re-identification dataset based on publicly available aerial object tracking datasets. By applying the optimized data association scheme, we outperform state-of-the-art trackers, as the number of ID-switches is considerably reduced.

Moving objects play a key role for gaining scene understanding in aerial surveillance tasks. The detection of moving vehicles can be challenging due to high object distance, simultaneous object and camera motion, shadows, or weak contrast. In scenarios where vehicles are driving on busy urban streets, this is even more challenging due to possible merged detections. In this paper, a video processing chain is proposed for moving vehicle detection and segmentation. The fundament for detecting motion which is independent of the camera motion is tracking of local image features such as Harris corners. Independently moving features are clustered. Since motion clusters are prone to merge similarly moving objects, we evaluate various object segmentation approaches based on contour extraction, blob extraction, or machine learning to handle such effects. We propose to use a local sliding window approach with Integral Channel Features (ICF) and AdaBoost classifier.

Automatic processing of videos coming from small UAVs offers high potential for advanced surveillance applications but is also very challenging. These challenges include camera motion, high object distance, varying object background, multiple objects near to each other, weak signal-to-noise-ratio (SNR), or compression artifacts. In this paper, a video processing chain for detection, segmentation, and tracking of multiple moving objects is presented dealing with the mentioned challenges. The fundament is the detection of local image features, which are not stationary. By clustering these features and subsequent object segmentation, regions are generated representing object hypotheses. Multi-object tracking is introduced using a Kalman filter and considering the camera motion. Split or merged object regions are handled by fusion of the regions and the local features. Finally, a quantitative evaluation of object segmentation and tracking is provided.

The reflection of light from most materials consists of two major terms: the specular and the diffuse.Specular reflection may be modeled from first principles by considering a rough surface consisting of perfect reflectors, or micro-facets.Diffuse reflection is generally considered to result from multiple scattering either from a rough surface or from within a layer near the surface.Accounting for diffuse reflection by Lambert's Cosine Law, as is universally done in computer graphics, is not a physical theory based on first principles.This paper presents a model for subsurface scattering in layered surfaces in terms of one-dimensional linear transport theory.We derive explicit formulas for backscattering and transmission that can be directly incorporated in most rendering systems, and a general Monte Carlo method that is easily added to a ray tracer.This model is particularly appropriate for common layered materials appearing in nature, such as biological tissues (e.g.skin, leaves, etc.) or inorganic materials (e.g.snow, sand, paint, varnished or dusty surfaces).As an application of the model, we simulate the appearance of a face and a cluster of leaves from experimental data describing their layer properties.

The author describes a visualization model for three-dimensional scalar data fields based on linear transport theory. The concept of virtual particles for the extraction of information from data fields in introduced. The role of different types of interaction of the data field with those particles such as absorption, scattering, source and color shift are discussed and demonstrated. Special attention is given to possible tools for the enhancement of interesting data features. Random texturing can provide visual insights as to the magnitude and distribution of deviations of related data fields, e.g., originating from analytic models, and measurements, or in the noise content of a given data field. Hidden symmetries of a data set can often be identified visually by allowing it to interact with a preselected beam of physical particles with the attendant appearance of characteristic structural effects such as channeling.>

A model for texturing of surfaces is introduced based on the concept of light intensity fluctuations. During the evaluation of the reflected intensity in the rendering process a non-Gaussian stochastic component is added which is governed by electromagnetic scattering theory. This component simulates the appearence of macroscopic surface irregularities in the image plane by considering not only the mean value of the intensity, given by the usual specular contribution, but also its variance and autocorrelation function. The variance generates the strength and distribution of the intensity fluctuations and the spatiotemporal auto-correlation function can be used to model the form and temporal development of the texture patterns. With an appropriate choice of a few parameters, soft intensity perturbations and bumpy speckle patterns as well as glint effects can be created.