Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Multi-modal Semantic Place Classification
International Journal of Robotics Research
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Fast unsupervised ego-action learning for first-person sports videos
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Detecting activities of daily living in first-person camera views
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Understanding egocentric activities
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Learning to recognize daily actions using gaze
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
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In this paper we study the problem of recognizing Instrumental Activities of Daily Living (IADL) in egocentric camera view. The target application of this research is the indexing of videos of patients with Alzehimer disease, thus providing medical staff with fast access and easy navigation through the video contents and helping them while assessing patients' abilities to perform IADL. Driven by the consideration that an activity in egocentric videos can be defined as a sequence of interacted objects inside different rooms, we present a novel representation based on the output of object and room detectors over temporal segments. In addition, our object detection approach is extended by automatic detection of visually salient regions since distinguishing active objects from context has been proven to dramatically improve performances in egocentric ADL recognition. We have assessed our proposal on a publicly available egocentric dataset and show extensive experimental results that demonstrate our approach outperforms current state of the art for unconstrained scenarios in which training and testing environments may be notably different.