Parameterized modeling and recognition of activities
Computer Vision and Image Understanding
View-Invariant Representation and Recognition of Actions
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Hybrid Models for Human Motion Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Behavior recognition via sparse spatio-temporal features
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Local descriptors for spatio-temporal recognition
SCVMA'04 Proceedings of the First international conference on Spatial Coherence for Visual Motion Analysis
A SURF-Based spatio-temporal feature for feature-fusion-based action recognition
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part I
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Recently spatio-temporal local features have been proposed as image features to recognize events or human actions in videos. In this paper, we propose yet another local spatio-temporal feature based on the SURF detector, which is a lightweight local feature. Our method consists of two parts: extracting visual features and extracting motion features. First, we select candidate points based on the SURF detector. Next, we calculate motion features at each point with local temporal units divided in order to consider consecutiveness of motions. Since our proposed feature is intended to be robust to rotation, we rotate optical flow vectors to the main direction of extracted SURF features. In the experiments, we evaluate the proposed spatio-temporal local feature with the common dataset containing six kinds of simple human actions. As the result, the accuracy achieves 86%, which is almost equivalent to state-of-the-art. In addition, we make experiments to classify large amounts of Web video clips downloaded from Youtube.