Alexandros Gavriilidis

Multisensor data fusion for advanced driver assistance systems - the Active Safety Car project

Driver assistance systems support overstrained and affected drivers and become more and more essential for series-production vehicles. Object detection and segmentation is one of the most challenging research topics in this field. In order to warn the driver or automatically break before a potential collision, objects intersecting the path of the host vehicle have to be detected and classified. Most recently developed approaches are based on two dimensional image processing, sometimes in combination with a tracking algorithm associating detections in consecutive frames to one and the same object. Further robustness is achieved by multisensor data fusion, i.e. information by two or more different sensors (e.g. camera and radar data) are fused in order to get a much more reliable result. Another aspect for safety applications is communication between cars, which provides additional sensor locations and thus also requires data fusion technology. Two different approaches for data fusion are proposed and first results are presented.

Applications for a people detection and tracking algorithm using a time-of-flight camera

This paper outlines a method and applications for detection and tracking of people in depth images, acquired with a low-resolution Time-of-Flight (ToF) camera. This depth sensor is placed perpendicular to the ground in order to provide distance information from a top-view position. Usage of intrinsic and extrinsic camera parameters allows estimation of a ground plane and comparison to the measured distances of the ToF sensor in every pixel. Differences to the expected ground plane define foreground information, that is subsequently combined to associated regions. These regions of interest (ROI) are analyzed to distinguish persons from other objects by using a matched filter that is applied the height segmented depth information of each of these regions. The proposed method separates crowds into individuals and facilitates a multi-object tracking system based on Kalman filtering. Furthermore, we present several applications for the proposed method. Experiments with different crowding situations - from very low to very high density - and different heights of camera placements have proven the applicability and practicability of the system.

Descending step classification using time-of-flight sensor data

This paper proposes a method to analyse human-made environments regarding the existence of descending stairs and steps to assists visually impaired and furthermore disabled people, that are not able to use traditional supports like blind canes. Those people are heavily limited in their daily lives, since wrong decisions caused by the lack of information can easily lead to accidents. We use depth data acquired with a low-resolution Time-of-Flight (ToF) camera to perceive the scene in front a mobile vehicle (rollator) to provide the user with detailed information about potentially hazardous situations. Experiments with affected persons have shown the ability of the system to help them understand the environment and, in particular, avoid falls from descending stairs.

Tomographical scene reconstruction in the active safety car project

Tomographical reconstruction algorithms can be applied to camera based measurements and thus reconstruct the scenery without knowledge about included objects. The latter is interesting in the domain of driver assistance systems that have to monitor the driveway independently from a priori knowledge about possibly appearing objects. The paper presents tomographical background information, the transfer from radiographing to visual light rays and its negative impacts, and some first result obtained by applying the presented algorithm to images of a from view camera.

People Detection and Tracking from a Top-View Position Using a Time-of-Flight Camera

This paper outlines a method for the detection and tracking of people in depth images, acquired with a low-resolution Time-of-Flight (ToF) camera. This depth sensor is placed perpendicular to the ground in order to provide distance information from a top-view position.

2-D signal theoretic investigation of background elimination in visual tomographic reconstruction for safety and enabling health applications

Visual tomography is a relatively new method for 3D scene reconstruction. It is adopted from medical tomography and based on multiple images from different viewpoints of a scene. In this context, multidimensional spectra and filtering techniques are the key technology for the reconstruction process. Visual tomography differs from classical tomography in several aspects which leads to new challenges with respect to mathematical description. The present paper examines the influence of image background on reconstruction quality. This background problem does not appear in classical medical tomography applications. In particular, the influence of multidimensional sampling and restrictions with respect to the number of view angles can be analyzed by using multidimensional signal theoretical concepts. The differences between ideal (no background) and real acquisition conditions are examined. Visual tomography has the potential for innovative new fields of applications, where Enabling Technologies for Societal Challenges are the focus of our considerations. Demographic change leads to a high interest for enabling mobility for elderly people with physical disabilities. Walking frames equipped with such technologies will be able to assist such people in real day environments.

RSSI-Based Real-Time Indoor Positioning Using ZigBee Technology for Security Applications

Localization in indoor environments is an important aspect with regard to mobile security applications. Because here, the global positioning system (GPS) is not available or very imprecise, other positioning systems are required. For that matter wireless sensor networks provide two common approaches based on received signal strength indicators (RSSI). The first one uses fingerprints and the second is based on trilateration. Because fingerprinting needs a lot of training and (re-)calibration, this paper presents a new indoor positioning system based on RSSIs and trilateration using ZigBee technology. Since RSSI measurements are very susceptible to noise, the gathered RSSIs have to be preprocessed before they can be used for position calculations. For this reason, the RSSIs were averaged using time-dependent weights and smoothed over time so that outliers and old RSSIs can be eliminated. The presented indoor positioning system was verified by experiments.

Classification of ascending steps and stairs using Time-of-Flight sensor data

This paper proposes a method to analyse human-made environments in order to verify the presence of ascending steps or stairs. Our system is intended to assist visually impaired people by providing acoustic information about the scene in front of a low-resolution Time-of-Flight (ToF) camera that is fixed to a mobile vehicle (rollator). Detailed instructions to the user about potentially hazardous situations are provided. This paper in particular deals with a fast approach on classification of ascending steps in 3D point clouds. This method is part of a system that aims on enhancing visually impaired persons understand the environment and help prevent collisions.

Application specific stability of 3-D Roesser-like model realizations

Stability of multidimensional (k-D) systems is still a challenging field of work. Well known and established stability measures may lead to complex mathematical problems, while simple tests are restricted to special cases of n-D systems. A new stability test for certain discrete 3-D system realizations given in a Roesser-like model description is proposed. This test is suitable for signals bounded with respect to all three coordinate directions, like spatio temporal video image signals. The 3-D system is observed in real operation, i.e. considering a sequence of processing, which leads to a 1-D state space description, allowing for application of a 1-D stability test. Since application of 1-D stability tests to higher dimensional problems is not a completely new approach, main contribution of this paper is the regular and well structured decomposition of a discrete 3-D system description into a classical 1-D state space description.

Evaluation of pedestrian detection fusion and localization based on the idea of car-to-X communication

Pedestrian safety applications for urban environments, e.g. observation of intersections or crosswalks for pedestrians, are a growing research area. This paper is focused on the fusion of detected pedestrians from different viewing angles, especially from a surveillance system and from a moving car. Therefore, a combination of the available information inside of a generated world map, which is based on the global positioning system (GPS), is presented. The model for transforming detections with assumed uncertainties into the world map, and an attempt to combine separate independent single detections from different views to a unique one are results of the paper. Besides the combination of information, an evaluation of the detection accuracy of a surveillance system and a monocular object detection driver assistance system is presented. This evaluation discloses drawbacks as well as benefits of an information mapping from different viewing angles into a global world map.