10,171 research outputs found
Unsupervised Action Proposal Ranking through Proposal Recombination
Recently, action proposal methods have played an important role in action
recognition tasks, as they reduce the search space dramatically. Most
unsupervised action proposal methods tend to generate hundreds of action
proposals which include many noisy, inconsistent, and unranked action
proposals, while supervised action proposal methods take advantage of
predefined object detectors (e.g., human detector) to refine and score the
action proposals, but they require thousands of manual annotations to train.
Given the action proposals in a video, the goal of the proposed work is to
generate a few better action proposals that are ranked properly. In our
approach, we first divide action proposal into sub-proposal and then use
Dynamic Programming based graph optimization scheme to select the optimal
combinations of sub-proposals from different proposals and assign each new
proposal a score. We propose a new unsupervised image-based actioness detector
that leverages web images and employs it as one of the node scores in our graph
formulation. Moreover, we capture motion information by estimating the number
of motion contours within each action proposal patch. The proposed method is an
unsupervised method that neither needs bounding box annotations nor video level
labels, which is desirable with the current explosion of large-scale action
datasets. Our approach is generic and does not depend on a specific action
proposal method. We evaluate our approach on several publicly available trimmed
and un-trimmed datasets and obtain better performance compared to several
proposal ranking methods. In addition, we demonstrate that properly ranked
proposals produce significantly better action detection as compared to
state-of-the-art proposal based methods
Skeleton-based Action Recognition of People Handling Objects
In visual surveillance systems, it is necessary to recognize the behavior of
people handling objects such as a phone, a cup, or a plastic bag. In this
paper, to address this problem, we propose a new framework for recognizing
object-related human actions by graph convolutional networks using human and
object poses. In this framework, we construct skeletal graphs of reliable human
poses by selectively sampling the informative frames in a video, which include
human joints with high confidence scores obtained in pose estimation. The
skeletal graphs generated from the sampled frames represent human poses related
to the object position in both the spatial and temporal domains, and these
graphs are used as inputs to the graph convolutional networks. Through
experiments over an open benchmark and our own data sets, we verify the
validity of our framework in that our method outperforms the state-of-the-art
method for skeleton-based action recognition.Comment: Accepted in WACV 201
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