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Machine Learning
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Data Mining and Knowledge Discovery
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Recognizing Human Actions: A Local SVM Approach
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Creating Efficient Codebooks for Visual Recognition
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Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
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Audio indexing: primary components retrieval
Multimedia Tools and Applications
HMM based structuring of tennis videos using visual and audio cues
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Audiovisual integration with Segment Models for tennis video parsing
Computer Vision and Image Understanding
Rushes summarization by IRIM consortium: redundancy removal and multi-feature fusion
TVS '08 Proceedings of the 2nd ACM TRECVid Video Summarization Workshop
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BCS-HCI '08 Proceedings of the 22nd British HCI Group Annual Conference on People and Computers: Culture, Creativity, Interaction - Volume 2
A novel sequence representation for unsupervised analysis of human activities
Artificial Intelligence
Robust Sequential Data Modeling Using an Outlier Tolerant Hidden Markov Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
On parsing visual sequences with the hidden Markov model
Journal on Image and Video Processing
A survey on vision-based human action recognition
Image and Vision Computing
Comparing evaluation protocols on the KTH dataset
HBU'10 Proceedings of the First international conference on Human behavior understanding
Event detection and recognition for semantic annotation of video
Multimedia Tools and Applications
Egocentric visual event classification with location-based priors
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Passively recognising human activities through lifelogging
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Double fusion for multimedia event detection
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
Activity recognition using an egocentric perspective of everyday objects
UIC'07 Proceedings of the 4th international conference on Ubiquitous Intelligence and Computing
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This paper presents a method for indexing activities of daily living in videos acquired from wearable cameras. It addresses the problematic of analyzing the complex multimedia data acquired from wearable devices, which has been recently a growing concern due to the increasing amount of this kind of multimedia data. In the context of dementia diagnosis by doctors, patient activities are recorded in the environment of their home using a lightweight wearable device, to be later visualized by the medical practitioners. The recording mode poses great challenges since the video data consists in a single sequence shot where strong motion and sharp lighting changes often appear. Because of the length of the recordings, tools for an efficient navigation in terms of activities of interest are crucial. Our work introduces a video structuring approach that combines automatic motion based segmentation of the video and activity recognition by a hierarchical two-level Hidden Markov Model. We define a multi-modal description space over visual and audio features, including mid-level features such as motion, location, speech and noise detections. We show their complementarities globally as well as for specific activities. Experiments on real data obtained from the recording of several patients at home show the difficulty of the task and the promising results of the proposed approach.