Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Pairwise classification and support vector machines
Advances in kernel methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Layered Representations for Human Activity Recognition
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Inferring Activities from Interactions with Objects
IEEE Pervasive Computing
Automatic Analysis of Multimodal Group Actions in Meetings
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Towards Automatic Body Language Annotation
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
ISWC '07 Proceedings of the 2007 11th IEEE International Symposium on Wearable Computers
A novel sequence representation for unsupervised analysis of human activities
Artificial Intelligence
Learning large scale common sense models of everyday life
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Proceedings of the 11th international conference on Ubiquitous computing
Learning and predicting multimodal daily life patterns from cell phones
Proceedings of the 2009 international conference on Multimodal interfaces
A long-term evaluation of sensing modalities for activity recognition
UbiComp '07 Proceedings of the 9th international conference on Ubiquitous computing
A practical approach to recognizing physical activities
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
Building reliable activity models using hierarchical shrinkage and mined ontology
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
The design of a portable kit of wireless sensors for naturalistic data collection
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
Using a live-in laboratory for ubiquitous computing research
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
IEEE Transactions on Information Technology in Biomedicine
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
From on-going to complete activity recognition exploiting related activities
HBU'10 Proceedings of the First international conference on Human behavior understanding
Identifying important action primitives for high level activity recognition
EuroSSC'10 Proceedings of the 5th European conference on Smart sensing and context
Person localization and soft authentication using an infrared ceiling sensor network
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part II
Internet of things: a review of literature and products
Proceedings of the 25th Australian Computer-Human Interaction Conference: Augmentation, Application, Innovation, Collaboration
Journal of Ambient Intelligence and Smart Environments - Design and Deployment of Intelligent Environments
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We propose and investigate a paradigm for activity recognition, distinguishing the "on-going activity" recognition task (OGA) from that addressing "complete activities" (CA). The former starts from a time interval and aims to discover which activities are going on inside it. The latter, in turn, focuses on terminated activities and amounts to taking an external perspective on activities. We argue that this distinction is quite natural and the OGA task has a number of interesting properties; e.g., the possibility of reconstructing complete activities in terms of on-going ones, the avoidance of the thorny issue of activity segmentation, and a straightforward accommodation of complex activities, etc. Moreover, some plausible properties of the OGA task are discussed and then investigated in a classification study, addressing: the dependence of classification performance on the duration of time windows and its relationship with actional types (homogeneous vs. non-homogeneous activities), and on the assortments of features used. Three types of visual features are exploited, obtained from a data set that tries to balance the pros and cons of laboratory-based and naturalistic ones. The results provide partial confirmation to the hypothesis and point to relevant open issues for future work.