44 research outputs found
Persistent Homology of Attractors For Action Recognition
In this paper, we propose a novel framework for dynamical analysis of human
actions from 3D motion capture data using topological data analysis. We model
human actions using the topological features of the attractor of the dynamical
system. We reconstruct the phase-space of time series corresponding to actions
using time-delay embedding, and compute the persistent homology of the
phase-space reconstruction. In order to better represent the topological
properties of the phase-space, we incorporate the temporal adjacency
information when computing the homology groups. The persistence of these
homology groups encoded using persistence diagrams are used as features for the
actions. Our experiments with action recognition using these features
demonstrate that the proposed approach outperforms other baseline methods.Comment: 5 pages, Under review in International Conference on Image Processin
Shape Parameter Estimation
Performance of machine learning approaches depends strongly on the choice of
misfit penalty, and correct choice of penalty parameters, such as the threshold
of the Huber function. These parameters are typically chosen using expert
knowledge, cross-validation, or black-box optimization, which are time
consuming for large-scale applications. We present a principled, data-driven
approach to simultaneously learn the model pa- rameters and the misfit penalty
parameters. We discuss theoretical properties of these joint inference
problems, and develop algorithms for their solution. We show synthetic examples
of automatic parameter tuning for piecewise linear-quadratic (PLQ) penalties,
and use the approach to develop a self-tuning robust PCA formulation for
background separation.Comment: 20 pages, 10 figure
Crowd Counting with Decomposed Uncertainty
Research in neural networks in the field of computer vision has achieved
remarkable accuracy for point estimation. However, the uncertainty in the
estimation is rarely addressed. Uncertainty quantification accompanied by point
estimation can lead to a more informed decision, and even improve the
prediction quality. In this work, we focus on uncertainty estimation in the
domain of crowd counting. With increasing occurrences of heavily crowded events
such as political rallies, protests, concerts, etc., automated crowd analysis
is becoming an increasingly crucial task. The stakes can be very high in many
of these real-world applications. We propose a scalable neural network
framework with quantification of decomposed uncertainty using a bootstrap
ensemble. We demonstrate that the proposed uncertainty quantification method
provides additional insight to the crowd counting problem and is simple to
implement. We also show that our proposed method exhibits the state of the art
performances in many benchmark crowd counting datasets.Comment: Accepted in AAAI 2020 (Main Technical Track