Paper Info
Title
“Important stuff, everywhere!” Activity recognition with salient proto-objects as context
Abstract
Object information is an important cue to discriminate between activities that draw part of their meaning from context. Most of current work either ignores this information or relies on specific object detectors. However, such object detectors require a significant amount of training data and complicate the transfer of the action recognition framework to novel domains with different objects and object-action relationships. Motivated by recent advances in saliency detection, we propose to employ salient proto-objects for unsupervised discovery of object- and object-part candidates and use them as a contextual cue for activity recognition. Our experimental evaluation on three publicly available data sets shows that the integration of proto-objects and simple motion features substantially improves recognition performance, outperforming the state-of-the-art.
Year
DOI
Venue
2014
10.1109/WACV.2014.6836041
Applications of Computer Vision
Keywords
Field
DocType
object detection,object recognition,action recognition framework,activity recognition,object detectors,object information,object-action relationships,saliency detection,salient proto-objects,training data
Training set,Computer vision,Data set,Activity recognition,Object detection object recognition,Pattern recognition,Salience (neuroscience),Computer science,Action recognition,Image segmentation,Artificial intelligence,Salient
Conference
ISSN
Citations 
PageRank 
2472-6737
5
0.39
References 
Authors
28
4
Name
Order
Citations
PageRank
Lukas Rybok150.39
Boris Schauerte261.08
Ziad Al-Halah3788.73
Rainer Stiefelhagen43512274.86