Simultaneous Visual Recognition of Manipulation Actions and Manipulated Objects

Authors:
Hedvig Kjellström;Javier Romero;David Martínez;Danica Kragić
Affiliations:
Computational Vision and Active Perception Lab, School of Computer Science and Communication, KTH, Stockholm, Sweden SE-100 44;Computational Vision and Active Perception Lab, School of Computer Science and Communication, KTH, Stockholm, Sweden SE-100 44;Computational Vision and Active Perception Lab, School of Computer Science and Communication, KTH, Stockholm, Sweden SE-100 44;Computational Vision and Active Perception Lab, School of Computer Science and Communication, KTH, Stockholm, Sweden SE-100 44
Venue:
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Year:
2008

Citing 0
Cited 9

Visual object-action recognition: Inferring object affordances from human demonstration

Computer Vision and Image Understanding
Robust sequence alignment for actor-object interaction recognition: Discovering actor-object states

Computer Vision and Image Understanding
On importance of interactions and context in human action recognition: Nataliya,,Shapovalova

IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Learning the semantics of object-action relations by observation

International Journal of Robotics Research
People watching: human actions as a cue for single view geometry

ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Scene semantics from long-term observation of people

ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Tracking in action space

ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part I
Tracking in object action space

Computer Vision and Image Understanding
Recognizing object manipulation activities using depth and visual cues

Journal of Visual Communication and Image Representation

Quantified Score

Hi-index	0.00

Visualization

Abstract

The visual analysis of human manipulation actions is of interest for e.g. human-robot interaction applications where a robot learns how to perform a task by watching a human. In this paper, a method for classifying manipulation actions in the context of the objects manipulated, and classifying objects in the context of the actions used to manipulate them is presented. Hand and object features are extracted from the video sequence using a segmentation based approach. A shape based representation is used for both the hand and the object. Experiments show this representation suitable for representing generic shape classes. The action-object correlation over time is then modeled using conditional random fields. Experimental comparison show great improvement in classification rate when the action-object correlation is taken into account, compared to separate classification of manipulation actions and manipulated objects.