334 research outputs found
Development of Hand Posture Classification and Food Constituent Estimation as Welfare Technology Using Convolutional Neural Network
2019岡山県立大å¦å¤§å¦
Faster VoxelPose: Real-time 3D Human Pose Estimation by Orthographic Projection
While the voxel-based methods have achieved promising results for
multi-person 3D pose estimation from multi-cameras, they suffer from heavy
computation burdens, especially for large scenes. We present Faster VoxelPose
to address the challenge by re-projecting the feature volume to the three
two-dimensional coordinate planes and estimating X, Y, Z coordinates from them
separately. To that end, we first localize each person by a 3D bounding box by
estimating a 2D box and its height based on the volume features projected to
the xy-plane and z-axis, respectively. Then for each person, we estimate
partial joint coordinates from the three coordinate planes separately which are
then fused to obtain the final 3D pose. The method is free from costly 3D-CNNs
and improves the speed of VoxelPose by ten times and meanwhile achieves
competitive accuracy as the state-of-the-art methods, proving its potential in
real-time applications.Comment: 22 pages, 7 figures, submitted to ECCV 202
Tracking-as-recognition for articulated full-body human motion analysis
This paper addresses the problem of markerless tracking of a human in full 3D with a high-dimensional (29D) body model Most work in this area has been focused on achieving accurate tracking in order to replace marker-based motion capture, but do so at the cost of relying on relatively clean observing conditions. This paper takes a different perspective, proposing a body-tracking model that is explicitly designed to handle real-world conditions such as occlusions by scene objects, failure recovery, long-term tracking, auto-initialisation, generalisation to different people and integration with action recognition. To achieve these goals, an action\u27s motions are modelled with a variant of the hierarchical hidden Markov model The model is quantitatively evaluated with several tests, including comparison to the annealed particle filter, tracking different people and tracking with a reduced resolution and frame rate.<br /
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