The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Recognizing Action at a Distance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Efficient Visual Event Detection Using Volumetric Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Principal Axis-Based Correspondence between Multiple Cameras for People Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A 3-dimensional sift descriptor and its application to action recognition
Proceedings of the 15th international conference on Multimedia
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
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Human action recognition in surveillance has become a hot topic in computer vision. In this paper, we develope a new method to recognize human action using motion information in video. Video sequence is compressed along time axis into a Motion Impression Image (MII), which is combined with two types of impression images from different views. One is a Period Impression Image (PII) by exploring the characteristics of the motion frequency. The other is an Optical Flow Impression Image (OFII) obtained from the analysis of motion mode. The proposed MII is a compact and time-invariant representation. Furthermore, it is simple and efficient to implement. After quantizing the combined MIIs, we feed them into a spatial pyramid matching kernel (SPMK) based classifier to recognize various human actions. At last, experiments on a known benchmark dataset demonstrate the better performance of the proposed approach against the state-of-the-art algorithms.