Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Inferring Activities from Interactions with Objects
IEEE Pervasive Computing
International Journal of Computer Vision
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
Learning to Recognize Activities from the Wrong View Point
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Learning situation models in a smart home
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
A long-term evaluation of sensing modalities for activity recognition
UbiComp '07 Proceedings of the 9th international conference on Ubiquitous computing
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation - Volume Part I
An efficient approach for multi-view human action recognition based on bag-of-key-poses
HBU'12 Proceedings of the Third international conference on Human Behavior Understanding
Automated behavioral mapping for monitoring social interactions among older adults
ICSR'12 Proceedings of the 4th international conference on Social Robotics
Silhouette-based human action recognition using sequences of key poses
Pattern Recognition Letters
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Recognizing activities in a home environment is challenging due to the variety of activities that can be performed at home and the complexity of the environment. Multiple cameras are usually needed to cover the whole observation area. This adds camera fusion as another challenge to activity recognition. We propose a hierarchical approach that recognizes both coarse-level and fine-level activities, in which different image features and learning methods are used for different activities based on their characteristics. The paper focuses on discussing the second-level of activity recognition with spatio-temporal features. Specifically, three fusion approaches for multiview activity recognition with spatio-temporal features are presented, including two decision fusion methods and one feature fusion method. They are comparatively analyzed in terms of their tradeoffs on assumptions on system setup, model transferability and recognition rate. Experiments show that challenging activities with subtle motions such as eating, cutting, scrambling, typing, reading etc. can be recognized with our approaches.