원문정보
초록
영어
Most of existed action recognition methods based on spatio-temporal descriptors have ignored their spatial distribution information. However the spatial distribution information usually is very useful to improve the discriminative ability of the motion representation. An improved spatio-temporal is proposed in this paper by combining local spatio-temporal feature and global positional distribution information (FEA) of interest points. Furthermore, in order to improve the classifier’s performance, an Adaboost-SVM method is utilized to recognize the human actions by using the proposed motion descriptor. The proposed recognition method is tested on the public dataset of KTH. The test results verified the proposed representation and recognition method can more accurately describe and recognize the human motion.
목차
1. Introduction
2. Feature Extraction and Representation
2.1. Interest Point Feature Extraction
2.2. Interest Point Feature Dimension Reduction
2.3 Position Distribution Information of Interest Points
2.4. The Combination of the Interest point feature and PDI
3. Action Recognition Based on Adaboost-SVM Classifiers
3.1. AdaBoost Algorithm [17-18]
3.2. Weak Classifiers Using SVM
4. Experiment and Results Analysis
4.1. Dataset
4.2. Experimental Results
5. Conclusion
Acknowledgements
References