Human Action Recognition Using Local Space Time Features and Adaboost Svm

Human action recognition has a wide range of promising applications like video surveillance, intelligent interface, and video retrieval. The objective of this project is to recognize and annotate different human activities present in digital videos taken from both constrained and unconstrained environment. This work extended the use of the techniques existing in object recognition in 2D images to video sequences by determining the Spatio-temporal Interest points using the extension of Harris operator in the time dimension. Features descriptors are computed on the cuboids around these interest points and further they are clustered and bag of features is built .SVM is used to classify the different classes of action present in the video. The recognition rate is further improved by using Adaboost SVM wherein number of weak classifier is weighted to form a strong classifier. The result shows that the proposed method using adaboost SVM classifier, the mean accuracy rate of recognition of KTH dataset is 89.13%.