3,585 research outputs found
A Multi-cut Formulation for Joint Segmentation and Tracking of Multiple Objects
Recently, Minimum Cost Multicut Formulations have been proposed and proven to
be successful in both motion trajectory segmentation and multi-target tracking
scenarios. Both tasks benefit from decomposing a graphical model into an
optimal number of connected components based on attractive and repulsive
pairwise terms. The two tasks are formulated on different levels of granularity
and, accordingly, leverage mostly local information for motion segmentation and
mostly high-level information for multi-target tracking. In this paper we argue
that point trajectories and their local relationships can contribute to the
high-level task of multi-target tracking and also argue that high-level cues
from object detection and tracking are helpful to solve motion segmentation. We
propose a joint graphical model for point trajectories and object detections
whose Multicuts are solutions to motion segmentation {\it and} multi-target
tracking problems at once. Results on the FBMS59 motion segmentation benchmark
as well as on pedestrian tracking sequences from the 2D MOT 2015 benchmark
demonstrate the promise of this joint approach
Robust Motion Segmentation from Pairwise Matches
In this paper we address a classification problem that has not been
considered before, namely motion segmentation given pairwise matches only. Our
contribution to this unexplored task is a novel formulation of motion
segmentation as a two-step process. First, motion segmentation is performed on
image pairs independently. Secondly, we combine independent pairwise
segmentation results in a robust way into the final globally consistent
segmentation. Our approach is inspired by the success of averaging methods. We
demonstrate in simulated as well as in real experiments that our method is very
effective in reducing the errors in the pairwise motion segmentation and can
cope with large number of mismatches
Robust Temporally Coherent Laplacian Protrusion Segmentation of 3D Articulated Bodies
In motion analysis and understanding it is important to be able to fit a
suitable model or structure to the temporal series of observed data, in order
to describe motion patterns in a compact way, and to discriminate between them.
In an unsupervised context, i.e., no prior model of the moving object(s) is
available, such a structure has to be learned from the data in a bottom-up
fashion. In recent times, volumetric approaches in which the motion is captured
from a number of cameras and a voxel-set representation of the body is built
from the camera views, have gained ground due to attractive features such as
inherent view-invariance and robustness to occlusions. Automatic, unsupervised
segmentation of moving bodies along entire sequences, in a temporally-coherent
and robust way, has the potential to provide a means of constructing a
bottom-up model of the moving body, and track motion cues that may be later
exploited for motion classification. Spectral methods such as locally linear
embedding (LLE) can be useful in this context, as they preserve "protrusions",
i.e., high-curvature regions of the 3D volume, of articulated shapes, while
improving their separation in a lower dimensional space, making them in this
way easier to cluster. In this paper we therefore propose a spectral approach
to unsupervised and temporally-coherent body-protrusion segmentation along time
sequences. Volumetric shapes are clustered in an embedding space, clusters are
propagated in time to ensure coherence, and merged or split to accommodate
changes in the body's topology. Experiments on both synthetic and real
sequences of dense voxel-set data are shown. This supports the ability of the
proposed method to cluster body-parts consistently over time in a totally
unsupervised fashion, its robustness to sampling density and shape quality, and
its potential for bottom-up model constructionComment: 31 pages, 26 figure
Unsupervised Object Discovery and Tracking in Video Collections
This paper addresses the problem of automatically localizing dominant objects
as spatio-temporal tubes in a noisy collection of videos with minimal or even
no supervision. We formulate the problem as a combination of two complementary
processes: discovery and tracking. The first one establishes correspondences
between prominent regions across videos, and the second one associates
successive similar object regions within the same video. Interestingly, our
algorithm also discovers the implicit topology of frames associated with
instances of the same object class across different videos, a role normally
left to supervisory information in the form of class labels in conventional
image and video understanding methods. Indeed, as demonstrated by our
experiments, our method can handle video collections featuring multiple object
classes, and substantially outperforms the state of the art in colocalization,
even though it tackles a broader problem with much less supervision
Unsupervised Deep Epipolar Flow for Stationary or Dynamic Scenes
Unsupervised deep learning for optical flow computation has achieved
promising results. Most existing deep-net based methods rely on image
brightness consistency and local smoothness constraint to train the networks.
Their performance degrades at regions where repetitive textures or occlusions
occur. In this paper, we propose Deep Epipolar Flow, an unsupervised optical
flow method which incorporates global geometric constraints into network
learning. In particular, we investigate multiple ways of enforcing the epipolar
constraint in flow estimation. To alleviate a "chicken-and-egg" type of problem
encountered in dynamic scenes where multiple motions may be present, we propose
a low-rank constraint as well as a union-of-subspaces constraint for training.
Experimental results on various benchmarking datasets show that our method
achieves competitive performance compared with supervised methods and
outperforms state-of-the-art unsupervised deep-learning methods.Comment: CVPR 201
Egocentric Hand Detection Via Dynamic Region Growing
Egocentric videos, which mainly record the activities carried out by the
users of the wearable cameras, have drawn much research attentions in recent
years. Due to its lengthy content, a large number of ego-related applications
have been developed to abstract the captured videos. As the users are
accustomed to interacting with the target objects using their own hands while
their hands usually appear within their visual fields during the interaction,
an egocentric hand detection step is involved in tasks like gesture
recognition, action recognition and social interaction understanding. In this
work, we propose a dynamic region growing approach for hand region detection in
egocentric videos, by jointly considering hand-related motion and egocentric
cues. We first determine seed regions that most likely belong to the hand, by
analyzing the motion patterns across successive frames. The hand regions can
then be located by extending from the seed regions, according to the scores
computed for the adjacent superpixels. These scores are derived from four
egocentric cues: contrast, location, position consistency and appearance
continuity. We discuss how to apply the proposed method in real-life scenarios,
where multiple hands irregularly appear and disappear from the videos.
Experimental results on public datasets show that the proposed method achieves
superior performance compared with the state-of-the-art methods, especially in
complicated scenarios
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