544 research outputs found
Unsupervised Video Understanding by Reconciliation of Posture Similarities
Understanding human activity and being able to explain it in detail surpasses
mere action classification by far in both complexity and value. The challenge
is thus to describe an activity on the basis of its most fundamental
constituents, the individual postures and their distinctive transitions.
Supervised learning of such a fine-grained representation based on elementary
poses is very tedious and does not scale. Therefore, we propose a completely
unsupervised deep learning procedure based solely on video sequences, which
starts from scratch without requiring pre-trained networks, predefined body
models, or keypoints. A combinatorial sequence matching algorithm proposes
relations between frames from subsets of the training data, while a CNN is
reconciling the transitivity conflicts of the different subsets to learn a
single concerted pose embedding despite changes in appearance across sequences.
Without any manual annotation, the model learns a structured representation of
postures and their temporal development. The model not only enables retrieval
of similar postures but also temporal super-resolution. Additionally, based on
a recurrent formulation, next frames can be synthesized.Comment: Accepted by ICCV 201
Visualization of interindividual differences in spinal dynamics in the presence of intraindividual variabilities
Surface topography systems enable the capture of
spinal dynamic movement. A visualization of possible unique
movement patterns appears to be difficult due to large intraclass and small inter-class variabilities. Therefore, we investigated
a visualization approach using Siamese neural networks (SNN)
and checked, if the identification of individuals is possible based
on dynamic spinal data. The presented visualization approach
seems promising in visualizing subjects in the presence of
intraindividual variability between different gait cycles as well
as day-to-day variability. Overall, the results indicate a possible
existence of a personal spinal ‘fingerprint’. The work forms the
basis for an objective comparison of subjects and the transfer of
the method to clinical use cases
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