715 research outputs found
VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera
We present the first real-time method to capture the full global 3D skeletal
pose of a human in a stable, temporally consistent manner using a single RGB
camera. Our method combines a new convolutional neural network (CNN) based pose
regressor with kinematic skeleton fitting. Our novel fully-convolutional pose
formulation regresses 2D and 3D joint positions jointly in real time and does
not require tightly cropped input frames. A real-time kinematic skeleton
fitting method uses the CNN output to yield temporally stable 3D global pose
reconstructions on the basis of a coherent kinematic skeleton. This makes our
approach the first monocular RGB method usable in real-time applications such
as 3D character control---thus far, the only monocular methods for such
applications employed specialized RGB-D cameras. Our method's accuracy is
quantitatively on par with the best offline 3D monocular RGB pose estimation
methods. Our results are qualitatively comparable to, and sometimes better
than, results from monocular RGB-D approaches, such as the Kinect. However, we
show that our approach is more broadly applicable than RGB-D solutions, i.e. it
works for outdoor scenes, community videos, and low quality commodity RGB
cameras.Comment: Accepted to SIGGRAPH 201
Multisensor-based human detection and tracking for mobile service robots
The one of fundamental issues for service robots is human-robot interaction. In order to perform such a task and provide the desired services, these robots need to detect and track people in the surroundings. In the present paper, we propose a solution for human tracking with a mobile robot that implements multisensor data fusion techniques. The system utilizes a new algorithm for laser-based legs detection using the on-board LRF. The approach is based on the recognition of typical leg patterns extracted from laser scans, which are shown to be very discriminative also in cluttered environments. These patterns can be used to localize both static and walking persons, even when the robot moves. Furthermore, faces are detected using the robot's camera and the information is fused to the legs position using a sequential implementation of Unscented Kalman Filter. The proposed solution is feasible for service robots with a similar device configuration and has been successfully implemented on two different mobile platforms.
Several experiments illustrate the effectiveness of our approach, showing that robust human tracking can be performed within complex indoor environments
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