3,540 research outputs found
Action Recognition in Videos: from Motion Capture Labs to the Web
This paper presents a survey of human action recognition approaches based on
visual data recorded from a single video camera. We propose an organizing
framework which puts in evidence the evolution of the area, with techniques
moving from heavily constrained motion capture scenarios towards more
challenging, realistic, "in the wild" videos. The proposed organization is
based on the representation used as input for the recognition task, emphasizing
the hypothesis assumed and thus, the constraints imposed on the type of video
that each technique is able to address. Expliciting the hypothesis and
constraints makes the framework particularly useful to select a method, given
an application. Another advantage of the proposed organization is that it
allows categorizing newest approaches seamlessly with traditional ones, while
providing an insightful perspective of the evolution of the action recognition
task up to now. That perspective is the basis for the discussion in the end of
the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4
table
Sparse-Dense Motion Modelling and Tracking for Manipulation without Prior Object Models
This work presents an approach for modelling and tracking previously unseen
objects for robotic grasping tasks. Using the motion of objects in a scene, our
approach segments rigid entities from the scene and continuously tracks them to
create a dense and sparse model of the object and the environment. While the
dense tracking enables interaction with these models, the sparse tracking makes
this robust against fast movements and allows to redetect already modelled
objects.
The evaluation on a dual-arm grasping task demonstrates that our approach 1)
enables a robot to detect new objects online without a prior model and to grasp
these objects using only a simple parameterisable geometric representation, and
2) is much more robust compared to the state of the art methods.Comment: IEEE Robotics and Automation Letters (RA-L) 202
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