204 research outputs found
Multi-view object tracking using sequential belief propagation
peer reviewedMultiple cameras and collaboration between them make possible the integration of information available from multiple views and reduce the uncertainty due to occlusions. This paper presents a novel method for integrating and tracking multi-view observations using bidirectional belief propagation. The method is based on a fully connected graphical model where target states at different views are represented as different but correlated random variables, and image observations at a given view are only associated with the target states at the same view. The tracking processes at different views collaborate with each other by exchanging information using a message passing scheme, which largely avoids propagating wrong information. An efficient sequential belief propagation algorithm is adopted to perform the collaboration and to infer the multi-view target states. We demonstrate the effectiveness of our method on video-surveillance sequences.TRICTRA
Differentiable Forward Kinematics for TensorFlow 2
Robotic systems are often complex and depend on the integration of a large
number of software components. One important component in robotic systems
provides the calculation of forward kinematics, which is required by both
motion-planning and perception related components. End-to-end learning systems
based on deep learning require passing gradients across component
boundaries.Typical software implementations of forward kinematics are not
differentiable, and thus prevent the construction of gradient-based, end-to-end
learning systems. In this paper we present a library compatible with ROS-URDF
that computes forward kinematics while simultaneously giving access to the
gradients w.r.t. joint configurations and model parameters, allowing
gradient-based learning and model identification. Our Python library is based
on Tensorflow~2 and is auto-differentiable. It supports calculating a large
number of kinematic configurations on the GPU in parallel, yielding a
considerable performance improvement compared to sequential CPU-based
calculation. https://github.com/lumoe/dlkinematics.gi
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