HUMAN ACTION RECOGNTION USING INTEREST POINT DETECTOR WITH KTH DATASET

Human action recognition and detection is very important in many application specially in security for monitoring and surveillance systems, for interacting field such as games, and interacting application, in this paper has focused on showing the challenges of capturing the video such as lighting, noise, scaling that exist in KTH data set, the propose method extracted corner, blob, and ridge interest point, as mention that all challenge of KTH has been tested, so the numbers of enter data are huge that's why the classification method has choosing is K-Nearest-neighbor( KNN) which works well with big data. The accuracy is 90% with this propose algorithm.