Recognizing Action at a Distance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Behavior recognition via sparse spatio-temporal features
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Robust Object Recognition with Cortex-Like Mechanisms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
Object Class Recognition and Localization Using Sparse Features with Limited Receptive Fields
International Journal of Computer Vision
Space-Time Shapelets for Action Recognition
WMVC '08 Proceedings of the 2008 IEEE Workshop on Motion and video Computing
Canonical Correlation Analysis of Video Volume Tensors for Action Categorization and Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Circuits and Systems Part I: Regular Papers
A pilot study on virtual camera control via Steady-State VEP in immersing virtual environments
HCI '08 Proceedings of the Third IASTED International Conference on Human Computer Interaction
An on-chip-trainable Gaussian-Kernel analog support vector machine
IEEE Transactions on Circuits and Systems Part I: Regular Papers
View-Independent Action Recognition from Temporal Self-Similarities
IEEE Transactions on Pattern Analysis and Machine Intelligence
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A VLSI-hardware-friendly action recognition algorithm using spatio-temporal motion-field patches has been developed. The system employs a hierarchical structure so that the robust recognition can be achieved gradually. At the lower level, motion fields representing local features such as speed and direction are directly calculated from video sequences and further blurred by max filters. At the higher level, a collection of so-called prototype patches are used to recognize query actions by comparing local features in the query videos with those prototypes. In addition, in order to design a system for real-time performance, we intentionally simplify all the calculations into summation operations or boolean operations so that the algorithm can be directly implemented on ultra high speed VLSI chips without much effort. Finally, We tested our system on a gesture perception database as well as widely used action recognition database, and promising recognition performance has been demonstrated.