Baysal, Sermetcan2016-01-082016-01-082011http://hdl.handle.net/11693/15162Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2011.Thesis (Master's) -- Bilkent University, 2011.Includes bibliographical references leaves 42-45.In this thesis, we utilize a line based pose representation to recognize human actions in videos. We represent the pose in each frame by employing a collection of line-pairs, so that limb and joint movements are better described and the geometrical relationships among the lines forming the human figure is captured. We contribute to the literature by proposing a new method that matches line-pairs of two poses to compute the similarity between them. Moreover, to encapsulate the global motion information of a pose sequence, we introduce line-flow histograms, which are extracted by matching line segments in consecutive frames. Experimental results on Weizmann and KTH datasets, emphasize the power of our pose representation; and show the effectiveness of using pose ordering and line-flow histograms together in grasping the nature of an action and distinguishing one from the others. Finally, we demonstrate the applicability of our approach to multi-camera systems on the IXMAS dataset.xi, 45 leaves, illustrationsEnglishinfo:eu-repo/semantics/openAccessHuman motionaction recognitionpose similaritypose matchingline-flowQP301 .B39 2011Human locomotion--Computer simulation.Body, Human--Computer simulation.Image processing--Digital techniques.Computer simulation.Digital computer vision.Pattern recognition systems.Thesis