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Detecting Activities of Daily Living in First-person Camera Views. Hamed Pirsiavash Deva Ramanan Department of Computer Science, University of California, Irvine fhpirsiav,dramanang@ics.uci.edu. Outline . Introduction Temporal pyramids Active object models Dataset Experimental results.
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Detecting Activities of Daily Living in First-person Camera Views Hamed Pirsiavash Deva Ramanan Department of Computer Science, University of California, Irvine fhpirsiav,dramanang@ics.uci.edu
Outline • Introduction • Temporal pyramids • Active object models • Dataset • Experimental results
Introduction • Application 1(Tele-rehabilitation) • Application 2(Life-logging) • Novel representations • Dataset
Novel representations • (1) temporal pyramids, which generalize the well-known spatial pyramid to approximate temporal correspondence when scoring a model • (2)composite object models that exploit the fact that objects look different when being interacted with.
Temporal pyramids • Use our models for activity recognition by learning linear SVM classifiers on features
Active object model • Recognizing objects undergoing hand manipulations is a crucial aspect of wearable ADL recognition.
Active object model • Augment the temporal pyramid feature from (4) to include K additional features corresponding to active objects • Refer this model as “AO”
Dataset-collection and size • Use a GoPro camera designed for wearable capture of athletes during sporting events.
Dataset-annotation • Action label
Dataset-annotation • Object bounding boxes
Dataset-annotation • Object identity • Human-object interaction
Dataset-characteristics • Functional taxonomy
Conclusion • Presented a novel dataset, algorithms, and empirical evaluation for the problem of detecting activities of daily living (ADL) in first-person camera views.
