Stefan Vacek

Sensor fusion for 3D human body tracking with an articulated 3D body model

This paper proposes a tracking system called VooDoo for 3D tracking of human body movements based on a 3D body model and the iterative closest point (ICP) algorithm. The proposed approach is able to incorporate raw data from different input sensors, as well as results from feature trackers in 2D or 3D. All input data is processed within the same model fitting step by modeling all input measurements in 3D model space. The system has been implemented and runs in realtime at appr. 10-14 Hz. Experiments with complex human movements exhibit the characteristics and advantages of the proposed approach

Modeling joint constraints for an articulated 3D human body model with artificial correspondences in ICP

This paper describes a new approach for modeling joints in an articulated 3D body model for tracking of the configuration of a human body. The used model consists of a set of rigid generalized cylinders. The joints between the cylinders are modeled as artificial point correspondences within the ICP (iterative closest point) tracking algorithm, which results in a set of forces and torques maintaining the model constraints. It is shown that different joint types with different degrees of freedom can be modeled with this approach. Experiments show the functionality and robustness of the presented model

Feature Set Selection and Optimal Classifier for Human Activity Recognition

Human activity recognition is an essential ability for service robots and other robotic systems which are in interaction with human beings. To be proactive, the system must be able to evaluate the current state of the user it is dealing with. Also future surveillance systems will benefit from robust activity recognition if realtime constraints are met, allowing to automate tasks that have to be fulfilled by humans yet. In this paper, a thorough analysis of features and classifiers aimed at human activity recognition is presented. Based on a set of 10 activities, the use of different feature selection algorithms is evaluated, as well as the results different classifiers (SVMs, Neural Networks, Bayesian Classifiers) provide in this context. Also the interdependency between feature selection method and chosen classifier is investigated. Furthermore, the optimal number of features to be used for an activity is examined.

Fusion of 2d and 3d sensor data for articulated body tracking

In this article, we present an approach for the fusion of 2d and 3d measurements for model-based person tracking, also known as Human Motion Capture. The applied body model is defined geometrically with generalized cylinders, and is set up hierarchically with connecting joints of different types. The joint model can be parameterized to control the degrees of freedom, adhesion and stiffness. This results in an articulated body model with constrained kinematic degrees of freedom. The fusion approach incorporates this model knowledge together with the measurements, and tracks the target body iteratively with an extended Iterative Closest Point (ICP) approach. Generally, the ICP is based on the concept of correspondences between measurements and model, which is normally exploited to incorporate 3d point cloud measurements. The concept has been generalized to represent and incorporate also 2d image space features. Together with the 3D point cloud from a 3d time-of-flight (ToF) camera, arbitrary features, derived from 2D camera images, are used in the fusion algorithm for tracking of the body. This gives complementary information about the tracked body, enabling not only tracking of depth motions but also turning movements of the human body, which is normally a hard problem for markerless human motion capture systems. The resulting tracking system, named VooDoo is used to track humans in a Human-Robot Interaction (HRI) context. We only rely on sensors on board the robot, i.e. the color camera, the ToF camera and a laser range finder. The system runs in realtime (~20 Hz) and is able to robustly track a human in the vicinity of the robot.

Situation classification for cognitive automobiles using case-based reasoning

Driving a car in urban areas autonomously requires the ability of an in-depth analysis of the current situation. For understanding the current situation and deducing consequences for the execution of behaviors (maneuvers), higher-level reasoning about the situation has to take place. In this paper, an approach for situation interpretation for cognitive automobiles is presented. The approach relies on case-based reasoning to predict the evolvement of the current situation and to select the appropriate behavior. Case-based reasoning allows to utilize prior experiences in the task of situation assessment.

Using case-based reasoning for autonomous vehicle guidance

Vehicle guidance in complex scenarios such as inner-city traffic requires an in-depth understanding of the current situation. In order to select the appropriate behavior for an autonomous vehicle, an analysis of the situation is needed. The analysis consists of an estimation of the situations development with respect to the selected behavior. This can only be done using higher-level reasoning techniques. In this paper, an approach for situation interpretation for autonomous vehicles is presented. The approach relies on case-based reasoning in order to predict the evolvement of the current situation and to select the appropriate behavior. Case-based reasoning allows to utilize prior experiences in the task of situation assessment.

An integrated simulation framework for cognitive automobiles

A cognitive automobile is a complex system. It is an indisputable fact that simulations are valuable tools for the development and testing of such complex systems. This paper presents an integrated closed-loop simulation framework which supports the development of a cognitive automobile. The framework aims at simulating complex traffic scenes in inner-city environments. The key features of the simulation are the generation of synthetic data for high level inference mechanisms, providing data for the analysis of car-to-car communication strategies and evaluation of cooperative vehicle behavior.

Rule-based tracking of multiple lanes using particle filters

Tracking of lanes is essential for intelligent vehicles in order to drive autonomously. A system is presented which allows tracking of multiple lanes. The system is based on a clear modelling of a lane and the parameter set of each lane is estimated using a particle filter which fuses different cues. A finite-state machine then decides whether or not a lane is really tracked. For each lane, a separate tracker is used and a set of rules controls the life-cycle of all trackers and keeps track of all the estimated lanes

Obstacle detection with a Photonic Mixing Device-camera in autonomous vehicles

In autonomous vehicles as well as in modern driver assistance systems, obstacle detection shows to be the most important task to be achieved. This paper presents a collision avoidance system, based on a modern Time-Of-Flight camera. These cameras allow a 3D perception of the environment, in which obstacles can be detected, independent of special features. Thus, the system is capable of all kinds of objects, including pedestrians as well as bicycles or vehicles. The used Photonic Mixing Device (PMD) camera has a measurement range of up to 50 m. The system is integrated into an autonomous vehicle, on which detected obstacles are investigated in detail. The vehicle steering commands are then generated by a behaviour network, depending on the presence of obstacles in the driving lane.

A CORBA-based distributed software architecture for control of service robots

This paper presents the distributed robot control software architecture developed for the autonomous service robot Albert2. The development of this architecture is focused on two major issues: modularity and the integration of learning aspects. Each module within the architecture is presented, as well as the underlying event-based communication framework. An approach for integration of learning capabilities is proposed.