Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
International Journal of Computer Vision
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
A 3-dimensional sift descriptor and its application to action recognition
Proceedings of the 15th international conference on Multimedia
An Efficient Dense and Scale-Invariant Spatio-Temporal Interest Point Detector
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
An overview of contest on semantic description of human activities (SDHA) 2010
ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
Variations of a hough-voting action recognition system
ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
Human detection using oriented histograms of flow and appearance
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Action recognition by dense trajectories
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Human activity prediction: Early recognition of ongoing activities from streaming videos
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
HMDB: A large video database for human motion recognition
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
In defense of soft-assignment coding
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Learning spatiotemporal graphs of human activities
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Hi-index | 0.00 |
Recently, human activity recognition has obtained increasing attention due to its wide range of potential applications. Much progress has been made to improve the performance on single actions in videos while few on collective and interactive activities. Human interaction is a more challenging task owing to multi-actors in an execution. In this paper, we utilize multi-scale dense trajectories and explore four advanced feature encoding methods on the human interaction dataset with a bag-of-features framework. Particularly, dense trajectories are described by shape, histogram of gradient orientation, histogram of flow orientation and motion boundary histogram, and all these are computed by integral images. Experimental results on the UT-Interaction dataset show that our approach outperforms state-of-the-art methods by 7-14%. Additionally, we thoroughly analyse a finding that the performance of vector quantization is on par with or even better than other sophisticated feature encoding methods by using dense trajectories in videos.