Human action recognition based on random spectral regression

Authors:
GuangFeng Lin;Hong Zhu;YinDi Fan;CaiXia Fan
Affiliations:
Information Science Department, Xi'an University of Technology, Xi'an, China;Electronic Information Engineering Department, Xi'an University of Technology, Xi'an, China;Shaanxi College of Communication Technology, Xi'an, China;Information Science Department, Xi'an University of Technology, Xi'an, China
Venue:
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
Year:
2011

Citing 11
Cited 0

The Recognition of Human Movement Using Temporal Templates

IEEE Transactions on Pattern Analysis and Machine Intelligence
Neighborhood Preserving Embedding

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)

Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Characteristic-Based Clustering for Time Series Data

Data Mining and Knowledge Discovery
Actions as Space-Time Shapes

IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual learning and recognition of sequential data manifolds with applications to human movement analysis

Computer Vision and Image Understanding
Learning to Recognize Activities from the Wrong View Point

ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
An Efficient Dense and Scale-Invariant Spatio-Temporal Interest Point Detector

ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Human action recognition by feature-reduced Gaussian process classification

Pattern Recognition Letters
A survey on vision-based human action recognition

Image and Vision Computing
Learning with l1-graph for image analysis

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

For solving the uncertain parameter selection, the highly spatiotemporal complexity and the difficulty of effectively extracting feature in manifold learning algorithm processing higher-dimension of human action sequence, human action recognition algorithm based on random spectral regression (RSPR) is presented. The algorithm has three steps. Firstly, according to uniform distribution of human action data in the manifold and the classification labels of human action, the weight matrix is built. This method overcomes the neighborhood parameter selection of the manifold learning algorithm. Secondly, by spectral regression, the spatial manifold based on frame is approximated, and the manifold mapping of unlabeled sample is obtained. At last, the feature of the temporal series is extracted in the spatial manifold based on frame, and then in Gaussian process classification the feature of human action is classified. The experiment has three parts. When RSPR tests the recognition of human action by leave-one-out crossvalidation in Weizmann database, the recognition rate reach 93.2%; comparing RSPR with locality preserving projection (LPP) and neighborhood preserving embedding (NPE), through extracting the statistical feature of temporal sequences RSPR shows better performance; in the test of walk action influenced RSPR displays better adaptability.