Making large-scale support vector machine learning practical
Advances in kernel methods
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
International Journal of Computer Vision
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
IEEE Transactions on Pattern Analysis and Machine Intelligence
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
An Efficient Dense and Scale-Invariant Spatio-Temporal Interest Point Detector
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Machine learning for high-speed corner detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Translating related words to videos and back through latent topics
Proceedings of the sixth ACM international conference on Web search and data mining
Human gesture recognition on product manifolds
The Journal of Machine Learning Research
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Recently, local descriptors have drawn a lot of attention as a representation method for action recognition. They are able to capture appearance and motion. They are robust to viewpoint and scale changes. They are easy to implement and quick to calculate. Moreover, they have shown to obtain good performance for action classification in videos. Over the last years, many different local spatio-temporal descriptors have been proposed. They are usually tested on different datasets and using different experimental methods. Moreover, experiments are done making assumptions that do not allow to fully evaluate descriptors. In this paper, we present a full evaluation of local spatio-temporal descriptors for action recognition in videos. Four widely used in state-of-the-art approaches descriptors and four video datasets were chosen. HOG, HOF, HOG-HOF and HOG3D were tested under a framework based on the bag-of-words model and Support Vector Machines.