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Novelty Detection from an Ego-Centric perspective
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
2011 (English)In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2011, 3297-3304 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper demonstrates a system for the automatic extraction of novelty in images captured from a small video camera attached to a subject's chest, replicating his visual perspective, while performing activities which are repeated daily. Novelty is detected when a (sub)sequence cannot be registered to previously stored sequences captured while performing the same daily activity. Sequence registration is performed by measuring appearance and geometric similarity of individual frames and exploiting the invariant temporal order of the activity. Experimental results demonstrate that this is a robust way to detect novelties induced by variations in the wearer's ego-motion such as stopping and talking to a person. This is an essentially new and generic way of automatically extracting information of interest to the camera wearer and can be used as input to a system for life logging or memory support.

Place, publisher, year, edition, pages
2011. 3297-3304 p.
Series
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, ISSN 1063-6919
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-38873DOI: 10.1109/CVPR.2011.5995731ISI: 000295615803073Scopus ID: 2-s2.0-80052890189ISBN: 978-145770394-2 (print)OAI: oai:DiVA.org:kth-38873DiVA: diva2:438237
Conference
2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011; Colorado Springs, CO; 20 June 2011 through 25 June 2011
Projects
VINST
Funder
ICT - The Next Generation
Note
QC 20111012Available from: 2011-09-01 Created: 2011-09-01 Last updated: 2014-06-04Bibliographically approved
In thesis
1. Data Driven Visual Recognition
Open this publication in new window or tab >>Data Driven Visual Recognition
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis is mostly about supervised visual recognition problems. Based on a general definition of categories, the contents are divided into two parts: one which models categories and one which is not category based. We are interested in data driven solutions for both kinds of problems.

In the category-free part, we study novelty detection in temporal and spatial domains as a category-free recognition problem. Using data driven models, we demonstrate that based on a few reference exemplars, our methods are able to detect novelties in ego-motions of people, and changes in the static environments surrounding them.

In the category level part, we study object recognition. We consider both object category classification and localization, and propose scalable data driven approaches for both problems. A mixture of parametric classifiers, initialized with a sophisticated clustering of the training data, is demonstrated to adapt to the data better than various baselines such as the same model initialized with less subtly designed procedures. A nonparametric large margin classifier is introduced and demonstrated to have a multitude of advantages in comparison to its competitors: better training and testing time costs, the ability to make use of indefinite/invariant and deformable similarity measures, and adaptive complexity are the main features of the proposed model.

We also propose a rather realistic model of recognition problems, which quantifies the interplay between representations, classifiers, and recognition performances. Based on data-describing measures which are aggregates of pairwise similarities of the training data, our model characterizes and describes the distributions of training exemplars. The measures are shown to capture many aspects of the difficulty of categorization problems and correlate significantly to the observed recognition performances. Utilizing these measures, the model predicts the performance of particular classifiers on distributions similar to the training data. These predictions, when compared to the test performance of the classifiers on the test sets, are reasonably accurate.

We discuss various aspects of visual recognition problems: what is the interplay between representations and classification tasks, how can different models better adapt to the training data, etc. We describe and analyze the aforementioned methods that are designed to tackle different visual recognition problems, but share one common characteristic: being data driven.

Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2014. ix, 36 p.
Keyword
Visual Recognition, Data Driven, Supervised Learning, Mixture Models, Non-Parametric Models, Category Recognition, Novelty Detection
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:kth:diva-145865 (URN)978-91-7595-197-3 (ISBN)
Public defence
2014-06-12, F3, Lindstedtsvägen 26, KTH, Stockholm, 14:00 (English)
Opponent
Supervisors
Note

QC 20140604

Available from: 2014-06-04 Created: 2014-06-02 Last updated: 2017-02-22Bibliographically approved

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Citation style
  • apa
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  • modern-language-association-8th-edition
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More styles
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  • sv-SE
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Output format
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