4,135 research outputs found
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
Detect-and-Track: Efficient Pose Estimation in Videos
This paper addresses the problem of estimating and tracking human body
keypoints in complex, multi-person video. We propose an extremely lightweight
yet highly effective approach that builds upon the latest advancements in human
detection and video understanding. Our method operates in two-stages: keypoint
estimation in frames or short clips, followed by lightweight tracking to
generate keypoint predictions linked over the entire video. For frame-level
pose estimation we experiment with Mask R-CNN, as well as our own proposed 3D
extension of this model, which leverages temporal information over small clips
to generate more robust frame predictions. We conduct extensive ablative
experiments on the newly released multi-person video pose estimation benchmark,
PoseTrack, to validate various design choices of our model. Our approach
achieves an accuracy of 55.2% on the validation and 51.8% on the test set using
the Multi-Object Tracking Accuracy (MOTA) metric, and achieves state of the art
performance on the ICCV 2017 PoseTrack keypoint tracking challenge.Comment: In CVPR 2018. Ranked first in ICCV 2017 PoseTrack challenge (keypoint
tracking in videos). Code: https://github.com/facebookresearch/DetectAndTrack
and webpage: https://rohitgirdhar.github.io/DetectAndTrack
DART: Distribution Aware Retinal Transform for Event-based Cameras
We introduce a generic visual descriptor, termed as distribution aware
retinal transform (DART), that encodes the structural context using log-polar
grids for event cameras. The DART descriptor is applied to four different
problems, namely object classification, tracking, detection and feature
matching: (1) The DART features are directly employed as local descriptors in a
bag-of-features classification framework and testing is carried out on four
standard event-based object datasets (N-MNIST, MNIST-DVS, CIFAR10-DVS,
NCaltech-101). (2) Extending the classification system, tracking is
demonstrated using two key novelties: (i) For overcoming the low-sample problem
for the one-shot learning of a binary classifier, statistical bootstrapping is
leveraged with online learning; (ii) To achieve tracker robustness, the scale
and rotation equivariance property of the DART descriptors is exploited for the
one-shot learning. (3) To solve the long-term object tracking problem, an
object detector is designed using the principle of cluster majority voting. The
detection scheme is then combined with the tracker to result in a high
intersection-over-union score with augmented ground truth annotations on the
publicly available event camera dataset. (4) Finally, the event context encoded
by DART greatly simplifies the feature correspondence problem, especially for
spatio-temporal slices far apart in time, which has not been explicitly tackled
in the event-based vision domain.Comment: 12 pages, revision submitted to TPAMI in Nov 201
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