Derinlik Bilgisi Kullanılarak İnsan Hareketlerinin Tanınması

Abstract

Human action recognition using depth sensors is an emerging technology especially in game console industry. Depth information provides 3D robust features about environments and increases accuracy of action recognition in short ranges. This thesis presents various approaches to recognize human actions using depth information obtained from the Microsoft Kinect RGBD sensor. In the first studied approach, information about angle and displacement of joints is obtained from a joint skeleton model to recognize actions. Then actions are considered as temporal patterns and studied on Hidden Markov Models and time series. In the Hidden Markov Model based model, actions are converted into observation series by utilizing a vocabulary constructed from the features. Besides actions are considered as time series and actions are classified after applying dimension reduction on features extracted from the series.