Efficient Min-cost Flow Tracking with Bounded Memory and Computation

This thesis is a contribution to solving multi-target tracking in an optimal fashion for real-time demanding computer vision applications. We introduce a challenging benchmark, recorded with our autonomous driving platform AnnieWAY. Three main challenges of tracking are addressed: Solving the data association (min-cost flow) problem faster than standard solvers, extending this approach to an online setting, and making it real-time capable by a tight approximation of the optimal solution

Mehrobjekt-Zustandsschätzung mit verteilten Sensorträgern am Beispiel der Umfeldwahrnehmung im Straßenverkehr

Umfeldwahrnehmung im automobilen Kontext kann als Zustandsschätzproblem mit mengenwertigem Systemzustand betrachtet werden. Basierend auf FISST wird eine SLAM-ähnliche Methodik gewählt, welche explizit die Unsicherheit bei der Lokalisierung des Sensorträgers berücksichtigt. Diese wird auf die PHD-, JIPDA- und MEMBER-Filteransätze angewandt. Hierbei ist eine Modifikation des Standardmessmodells nötig, um zu implementierbaren Korrekturgleichungen zu gelangen

Mehrobjekt-Zustandsschätzung mit verteilten Sensorträgern am Beispiel der Umfeldwahrnehmung im Straßenverkehr

Umfeldwahrnehmung im automobilen Kontext kann als Zustandsschätzproblem mit mengenwertigem Systemzustand betrachtet werden. Basierend auf FISST wird eine SLAM-ähnliche Methodik gewählt, welche explizit die Unsicherheit bei der Lokalisierung des Sensorträgers berücksichtigt. Diese wird auf die PHD-, JIPDA- und MEMBER-Filteransätze angewandt. Hierbei ist eine Modifikation des Standardmessmodells nötig, um zu implementierbaren Korrekturgleichungen zu gelangen

Fusion of Data from Heterogeneous Sensors with Distributed Fields of View and Situation Evaluation for Advanced Driver Assistance Systems

In order to develop a driver assistance system for pedestrian protection, pedestrians in the environment of a truck are detected by radars and a camera and are tracked across distributed fields of view using a Joint Integrated Probabilistic Data Association filter. A robust approach for prediction of the system vehicles trajectory is presented. It serves the computation of a probabilistic collision risk based on reachable sets where different sources of uncertainty are taken into account

Bayesian multi-target tracking: application to total internal reflection fluorescence microscopy

This thesis focuses on the problem of automated tracking of tiny cellular and sub-cellular structures, known as particles, in the sequences acquired from total internal reflection fluorescence microscopy (TIRFM) imaging technique. Our primary biological motivation is to develop an automated system for tracking the sub-cellular structures involving exocytosis (an intracellular mechanism) which is helpful for studying the possible causes of the defects in diseases such as diabetes and obesity. However, all methods proposed in this thesis are generalized to be applicable for a wide range of particle tracking applications. A reliable multi-particle tracking method should be capable of tracking numerous similar objects in the presence of high levels of noise, high target density and complex motions and interactions. In this thesis, we choose the Bayesian filtering framework as our main approach to deal with this problem. We focus on the approaches that work based on detections. Therefore, in this thesis, we first propose a method that robustly detects the particles in the noisy TIRFM sequences with inhomogeneous and time-varying background. In order to evaluate our detection and tracking methods on the sequences with known and reliable ground truth, we also present a framework for generating realistic synthetic TIRFM data. To propose a reliable multi-particle tracking method for TIRFM sequences, we suggest a framework by combining two robust Bayesian filters, the interacting multiple model and joint probabilistic data association (IMM-JPDA) filters. The performance of our particle tracking method is compared against those of several popular and state-of-the art particle tracking approaches on both synthetic and real sequences. Although our approach performs well in tracking particles, it can be very computationally demanding for the applications with dense targets with poor detections. To propose a computationally cheap, but reliable, multi-particle tracking method, we investigate the performance of a recent multi-target Bayesian filter based on random finite theory, the probability hypothesis density (PHD) filter, on our application. To this end, we propose a general framework for tracking particles using this filter. Moreover, we assess the performance of our proposed PHD filter on both synthetic and real sequences with high level of noise and particle density. We compare its results from both aspects of accuracy and processing time against our IMM-JPDA filter. Finally, we suggest a framework for tracking particles in a challenging problem where the noise characteristic and the background intensity of sequences change during the acquisition process which make detection profile and clutter rate time-variant. To deal with this, we propose a bootstrap filter using another type of the random finite set based Bayesian filters, the cardinalized PHD (CPHD) filter, composed of an estimator and a tracker. The estimator adaptively estimates the required meta parameters for the tracker such as clutter rate and the detection probability while the tracker estimates the state of the targets. We evaluate the performance of our bootstrap on both synthetic and real sequences under these time-varying conditions. Moreover, its performance is compared against those of our other particle trackers as well as the state-of-the art particle tracking approaches

Videogestützte Umfelderfassung zur Interpretation von Verkehrssituationen für kognitive Automobile

Es wird einen holistischer Ansatz zur Interpretation von Verkehrssituationen vorgestellt, der aus den drei Teilen Umfelderfassung, Wissensmodellierung und Situationsinterpretation besteht. Die Umfelderfassung dient dazu, das Umfeld des Fahrzeug durch unterschiedliche Sensorik zu beobachten und die zur Fahrzeugführung relevanten Informationen zu extrahieren. Mit Hilfe einer Ontologie werden Situationen beschrieben und durch das Fallbasierte Schließen klassifiziert und bewertet