Detecting eye contact using wearable eye-tracking glasses
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Learning to recognize daily actions using gaze
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Efficiently Scaling up Crowdsourced Video Annotation
International Journal of Computer Vision
A spatio-temporal pyramid matching for video retrieval
Computer Vision and Image Understanding
Proceedings of the 1st ACM international workshop on Multimedia indexing and information retrieval for healthcare
Human action recognition by fast dense trajectories
Proceedings of the 21st ACM international conference on Multimedia
Analyzing growing plants from 4D point cloud data
ACM Transactions on Graphics (TOG)
Effective 3D action recognition using EigenJoints
Journal of Visual Communication and Image Representation
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We present a novel dataset and novel algorithms for the problem of detecting activities of daily living (ADL) in firstperson camera views. We have collected a dataset of 1 million frames of dozens of people performing unscripted, everyday activities. The dataset is annotated with activities, object tracks, hand positions, and interaction events. ADLs differ from typical actions in that they can involve long-scale temporal structure (making tea can take a few minutes) and complex object interactions (a fridge looks different when its door is open). We develop novel representations including (1) temporal pyramids, which generalize the well-known spatial pyramid to approximate temporal correspondence when scoring a model and (2) composite object models that exploit the fact that objects look different when being interacted with. We perform an extensive empirical evaluation and demonstrate that our novel representations produce a two-fold improvement over traditional approaches. Our analysis suggests that real-world ADL recognition is “all about the objects,” and in particular, “all about the objects being interacted with.”