18,708 research outputs found
3D Tracking Using Multi-view Based Particle Filters
Visual surveillance and monitoring of indoor environments using multiple cameras has become a field of great activity in computer vision. Usual 3D tracking and positioning systems rely on several independent 2D tracking modules applied over individual camera streams, fused using geometrical relationships across cameras. As 2D tracking systems suffer inherent difficulties due to point of view limitations (perceptually similar foreground and background regions causing fragmentation of moving objects, occlusions), 3D tracking based on partially erroneous 2D tracks are likely to fail when handling multiple-people interaction. To overcome this problem, this paper proposes a Bayesian framework for combining 2D low-level cues from multiple cameras directly into the 3D world through 3D Particle Filters. This method allows to estimate the probability of a certain volume being occupied by a moving object, and thus to segment and track multiple people across the monitored area. The proposed method is developed on the basis of simple, binary 2D moving region segmentation on each camera, considered as different state observations. In addition, the method is proved well suited for integrating additional 2D low-level cues to increase system robustness to occlusions: in this line, a naïve color-based (HSI) appearance model has been integrated, resulting in clear performance improvements when dealing with complex scenarios
Finding Temporally Consistent Occlusion Boundaries in Videos using Geometric Context
We present an algorithm for finding temporally consistent occlusion
boundaries in videos to support segmentation of dynamic scenes. We learn
occlusion boundaries in a pairwise Markov random field (MRF) framework. We
first estimate the probability of an spatio-temporal edge being an occlusion
boundary by using appearance, flow, and geometric features. Next, we enforce
occlusion boundary continuity in a MRF model by learning pairwise occlusion
probabilities using a random forest. Then, we temporally smooth boundaries to
remove temporal inconsistencies in occlusion boundary estimation. Our proposed
framework provides an efficient approach for finding temporally consistent
occlusion boundaries in video by utilizing causality, redundancy in videos, and
semantic layout of the scene. We have developed a dataset with fully annotated
ground-truth occlusion boundaries of over 30 videos ($5000 frames). This
dataset is used to evaluate temporal occlusion boundaries and provides a much
needed baseline for future studies. We perform experiments to demonstrate the
role of scene layout, and temporal information for occlusion reasoning in
dynamic scenes.Comment: Applications of Computer Vision (WACV), 2015 IEEE Winter Conference
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Optical Flow in Mostly Rigid Scenes
The optical flow of natural scenes is a combination of the motion of the
observer and the independent motion of objects. Existing algorithms typically
focus on either recovering motion and structure under the assumption of a
purely static world or optical flow for general unconstrained scenes. We
combine these approaches in an optical flow algorithm that estimates an
explicit segmentation of moving objects from appearance and physical
constraints. In static regions we take advantage of strong constraints to
jointly estimate the camera motion and the 3D structure of the scene over
multiple frames. This allows us to also regularize the structure instead of the
motion. Our formulation uses a Plane+Parallax framework, which works even under
small baselines, and reduces the motion estimation to a one-dimensional search
problem, resulting in more accurate estimation. In moving regions the flow is
treated as unconstrained, and computed with an existing optical flow method.
The resulting Mostly-Rigid Flow (MR-Flow) method achieves state-of-the-art
results on both the MPI-Sintel and KITTI-2015 benchmarks.Comment: 15 pages, 10 figures; accepted for publication at CVPR 201
Self-Supervised Relative Depth Learning for Urban Scene Understanding
As an agent moves through the world, the apparent motion of scene elements is
(usually) inversely proportional to their depth. It is natural for a learning
agent to associate image patterns with the magnitude of their displacement over
time: as the agent moves, faraway mountains don't move much; nearby trees move
a lot. This natural relationship between the appearance of objects and their
motion is a rich source of information about the world. In this work, we start
by training a deep network, using fully automatic supervision, to predict
relative scene depth from single images. The relative depth training images are
automatically derived from simple videos of cars moving through a scene, using
recent motion segmentation techniques, and no human-provided labels. This proxy
task of predicting relative depth from a single image induces features in the
network that result in large improvements in a set of downstream tasks
including semantic segmentation, joint road segmentation and car detection, and
monocular (absolute) depth estimation, over a network trained from scratch. The
improvement on the semantic segmentation task is greater than those produced by
any other automatically supervised methods. Moreover, for monocular depth
estimation, our unsupervised pre-training method even outperforms supervised
pre-training with ImageNet. In addition, we demonstrate benefits from learning
to predict (unsupervised) relative depth in the specific videos associated with
various downstream tasks. We adapt to the specific scenes in those tasks in an
unsupervised manner to improve performance. In summary, for semantic
segmentation, we present state-of-the-art results among methods that do not use
supervised pre-training, and we even exceed the performance of supervised
ImageNet pre-trained models for monocular depth estimation, achieving results
that are comparable with state-of-the-art methods
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