9,725 research outputs found
Log-Euclidean Bag of Words for Human Action Recognition
Representing videos by densely extracted local space-time features has
recently become a popular approach for analysing actions. In this paper, we
tackle the problem of categorising human actions by devising Bag of Words (BoW)
models based on covariance matrices of spatio-temporal features, with the
features formed from histograms of optical flow. Since covariance matrices form
a special type of Riemannian manifold, the space of Symmetric Positive Definite
(SPD) matrices, non-Euclidean geometry should be taken into account while
discriminating between covariance matrices. To this end, we propose to embed
SPD manifolds to Euclidean spaces via a diffeomorphism and extend the BoW
approach to its Riemannian version. The proposed BoW approach takes into
account the manifold geometry of SPD matrices during the generation of the
codebook and histograms. Experiments on challenging human action datasets show
that the proposed method obtains notable improvements in discrimination
accuracy, in comparison to several state-of-the-art methods
LCrowdV: Generating Labeled Videos for Simulation-based Crowd Behavior Learning
We present a novel procedural framework to generate an arbitrary number of
labeled crowd videos (LCrowdV). The resulting crowd video datasets are used to
design accurate algorithms or training models for crowded scene understanding.
Our overall approach is composed of two components: a procedural simulation
framework for generating crowd movements and behaviors, and a procedural
rendering framework to generate different videos or images. Each video or image
is automatically labeled based on the environment, number of pedestrians,
density, behavior, flow, lighting conditions, viewpoint, noise, etc.
Furthermore, we can increase the realism by combining synthetically-generated
behaviors with real-world background videos. We demonstrate the benefits of
LCrowdV over prior lableled crowd datasets by improving the accuracy of
pedestrian detection and crowd behavior classification algorithms. LCrowdV
would be released on the WWW
Geodesic Distance Histogram Feature for Video Segmentation
This paper proposes a geodesic-distance-based feature that encodes global
information for improved video segmentation algorithms. The feature is a joint
histogram of intensity and geodesic distances, where the geodesic distances are
computed as the shortest paths between superpixels via their boundaries. We
also incorporate adaptive voting weights and spatial pyramid configurations to
include spatial information into the geodesic histogram feature and show that
this further improves results. The feature is generic and can be used as part
of various algorithms. In experiments, we test the geodesic histogram feature
by incorporating it into two existing video segmentation frameworks. This leads
to significantly better performance in 3D video segmentation benchmarks on two
datasets
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