We present a hierarchical grid-based, globally optimal tracking-by-detection approach to track an unknown number of targets in complex and dense scenarios, particularly addressing the challenges of complex interaction and mutual occlusion. Frame-by-frame detection is performed by hierarchical likelihood grids, matching shape templates through a fast oriented distance transform. To allow recovery from misdetections, common heuristics such as nonmaxima suppression within observations is eschewed. Within a discretized state-space, the data association problem is formulated as a grid-based network flow model, resulting in a convex problem casted into an integer linear programming form, giving a global optimal solution. In addition, we show how a behavior cue (body orientation) can be integrated into our association affinity model, providing valuable hints for resolving ambiguities between crossing trajectories. Unlike traditional motion-based approaches, we estimate body orientation by a hybrid methodology, which combines the merits of motion-based and 3D appearance-based orientation estimation, thus being capable of dealing also with still-standing or slowly moving targets. The performance of our method is demonstrated through experiments on a large variety of benchmark video sequences, including both indoor and outdoor scenarios.

In this paper, we present a system for controlling a quadrocopter using both optical and inertial measurements. We show how to use external stereo camera measurements for visual servoing, by onboard fusion at high rates, only natural features provided by the vehicle and without any active marker. In our experiments, we show the accuracy and robustness of our system during indoor flights, as well as robustness to external flight disturbances.

This paper presents recent developments in the area of visual tracking methodologies for an applied real-time person localization system, which primary aims to robust and failure-safe robotic camera control. We applied the described methods to virtual-reality TV broadcasting studio environments in Germany in order to close a gap in TV studio automation. The presented approach uses robot camera systems based on industrial robots in order to allow high-precision camera manipulation for virtual TV studios, without limiting the degrees of freedom that a robot manipulator can provide. To take robotic automation in TV studios to a completely new dimension, we have imparted intelligence to the system by tracking the TV presenter in real-time, allowing him or her to move naturally and freely within the TV studio, while maintaining the required scene parameters, such as position in the scene, zoom, focus, etc. according to prior defined scene behaviors. The tracking system itself is distributed and has proven to be scalable to multiple robotic camera systems operating synchronously in real-world studios.

No abstract available.

Both Robots and Personal Computers established new markets about 30 years ago and were enabling factors in Automation and Information Technology. However, while you can see Personal Computers in almost every home nowadays, the domain of Robots in general still is mostly restricted to industrial automation. Due to the physical impact of robots, a safe design is essential, which most robots still lack of and therefore prevent their application for personal use, although a slow change can be noticed by the introduction of dedicated robots for specific tasks, which can be classified as service robots. Moreover, as more and more robots are designed as service robots, their developers face the challenge of reducing the machines' complexity and providing smart user interface methods. Ideally the robot would be able to cooperate with a human, just like another human would.

Direct physical human-robot interaction has become a central part in the research field of robotics today. To use the advantages of the potential for humans and robots to work together as a team in industrial settings, the most important issues are safety for the human and an easy way to describe tasks for the robot. In this work, we present an approach of a hierarchical structured control of industrial robots for joint-action scenarios. Multiple atomic tasks including dynamic collision avoidance, operational position, and posture can be combined in an arbitrary order respecting constraints of higher priority tasks. The controller flow is based on the theory of orthogonal projection using nullspaces and constraint least-square optimization. To proof the approach, we present three collaboration scenarios between a human and an industrial robot.

In this paper, an integrated multi-camera person tracking system for virtual reality television studios (VR-TV) is presented. The system robustly tracks the moderator while freely moving, sitting or walking around the studio, and the estimation result can be used in order to drive the main broadcasting camera mounted on a large robotic arm. Application of the proposed tracking system to real-time VR-TV results in an autonomous robot cameraman, able to keep the moderator inside the screen with jitter-free viewpoint adjustments, as required by the scene rendering engine.

In this paper we present an efficient and robust real-time system for object contour tracking in image sequences. The developed application partly relies on an optimized implementation of a state-of-the-art curve fitting algorithm, and integrates important additional features in order to achieve robustness while keeping the speed of the main estimation algorithm. An application program has been developed, which requires only a few standard libraries available on most platforms, and runs at video frame rate on a common PC with standard hardware equipment.

The problem of grasping and manipulating rigid objects using a multifingered robotic system is dealt with. A hierarchical two-level closed-loop trajectory tracking control strategy is presented in order to achieve the manipulation task, together with a complimentary force control, for object grasping, which makes use of a formulation of grasping forces decomposition.