Local Temporal Block Difference Pattern for Action Recognition in Surveillance Videos using Tree Based Classifiers

Abstract

Intelligent video classification and prediction is a fundamental step towards effective retrieval system. Huge volume of video is available for navigation today and managing such video and prediction of the activity before its completion gains importance in video surveillance,human computer recognition, gesture recognition etc., An eminent Local Temporal Block Difference Pattern (LTBDP) is introducedwhich enable efficient feature extraction that could be given to Tree Classifiers like Random Forest and REPTree for further prediction.The proposed pattern has been evaluated on UT-interaction dataset which enable researchers to predict ongoing human actions in an efficient manner. Experimental results using LTBDP in Random Forest and REPTree classifier gives 85.6% and 66.45% accuracy respectively.