12,885 research outputs found
CoMaL Tracking: Tracking Points at the Object Boundaries
Traditional point tracking algorithms such as the KLT use local 2D
information aggregation for feature detection and tracking, due to which their
performance degrades at the object boundaries that separate multiple objects.
Recently, CoMaL Features have been proposed that handle such a case. However,
they proposed a simple tracking framework where the points are re-detected in
each frame and matched. This is inefficient and may also lose many points that
are not re-detected in the next frame. We propose a novel tracking algorithm to
accurately and efficiently track CoMaL points. For this, the level line segment
associated with the CoMaL points is matched to MSER segments in the next frame
using shape-based matching and the matches are further filtered using
texture-based matching. Experiments show improvements over a simple
re-detect-and-match framework as well as KLT in terms of speed/accuracy on
different real-world applications, especially at the object boundaries.Comment: 10 pages, 10 figures, to appear in 1st Joint BMTT-PETS Workshop on
Tracking and Surveillance, CVPR 201
MonoPerfCap: Human Performance Capture from Monocular Video
We present the first marker-less approach for temporally coherent 3D
performance capture of a human with general clothing from monocular video. Our
approach reconstructs articulated human skeleton motion as well as medium-scale
non-rigid surface deformations in general scenes. Human performance capture is
a challenging problem due to the large range of articulation, potentially fast
motion, and considerable non-rigid deformations, even from multi-view data.
Reconstruction from monocular video alone is drastically more challenging,
since strong occlusions and the inherent depth ambiguity lead to a highly
ill-posed reconstruction problem. We tackle these challenges by a novel
approach that employs sparse 2D and 3D human pose detections from a
convolutional neural network using a batch-based pose estimation strategy.
Joint recovery of per-batch motion allows to resolve the ambiguities of the
monocular reconstruction problem based on a low dimensional trajectory
subspace. In addition, we propose refinement of the surface geometry based on
fully automatically extracted silhouettes to enable medium-scale non-rigid
alignment. We demonstrate state-of-the-art performance capture results that
enable exciting applications such as video editing and free viewpoint video,
previously infeasible from monocular video. Our qualitative and quantitative
evaluation demonstrates that our approach significantly outperforms previous
monocular methods in terms of accuracy, robustness and scene complexity that
can be handled.Comment: Accepted to ACM TOG 2018, to be presented on SIGGRAPH 201
Learning to Transform Time Series with a Few Examples
We describe a semi-supervised regression algorithm that learns to transform one time series into another time series given examples of the transformation. This algorithm is applied to tracking, where a time series of observations from sensors is transformed to a time series describing the pose of a target. Instead of defining and implementing such transformations for each tracking task separately, our algorithm learns a memoryless transformation of time series from a few example input-output mappings. The algorithm searches for a smooth function that fits the training examples and, when applied to the input time series, produces a time series that evolves according to assumed dynamics. The learning procedure is fast and lends itself to a closed-form solution. It is closely related to nonlinear system identification and manifold learning techniques. We demonstrate our algorithm on the tasks of tracking RFID tags from signal strength measurements, recovering the pose of rigid objects, deformable bodies, and articulated bodies from video sequences. For these tasks, this algorithm requires significantly fewer examples compared to fully-supervised regression algorithms or semi-supervised learning algorithms that do not take the dynamics of the output time series into account
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