Browsing by Subject "action recognition"

Now showing 1 - 2 of 2

Open Access
A line based pose representation for human action recognition
(2011) Baysal, Sermetcan
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.
Open Access
Understanding human motion : recognition and retrieval of human activities
(2008) İkizler, Nazlı
Within the ever-growing video archives is a vast amount of interesting information regarding human action/activities. In this thesis, we approach the problem of extracting this information and understanding human motion from a computer vision perspective. We propose solutions for two distinct scenarios, ordered from simple to complex. In the first scenario, we deal with the problem of single action recognition in relatively simple settings. We believe that human pose encapsulates many useful clues for recognizing the ongoing action, and we can represent this shape information for 2D single actions in very compact forms, before going into details of complex modeling. We show that high-accuracy single human action recognition is possible 1) using spatial oriented histograms of rectangular regions when the silhouette is extractable, 2) using the distribution of boundary-fitted lines when the silhouette information is missing. We demonstrate that, inside videos, we can further improve recognition accuracy by means of adding local and global motion information. We also show that within a discriminative framework, shape information is quite useful even in the case of human action recognition in still images. Our second scenario involves recognition and retrieval of complex human activities within more complicated settings, like the presence of changing background and viewpoints. We describe a method of representing human activities in 3D that allows a collection of motions to be queried without examples, using a simple and effective query language. Our approach is based on units of activity at segments of the body, that can be composed across time and across the body to produce complex queries. The presence of search units is inferred automatically by tracking the body, lifting the tracks to 3D and comparing to models trained using motion capture data. Our models of short time scale limb behaviour are built using labelled motion capture set. Our query language makes use of finite state automata and requires simple text encoding and no visual examples. We show results for a large range of queries applied to a collection of complex motion and activity. We compare with discriminative methods applied to tracker data; our method offers significantly improved performance. We show experimental evidence that our method is robust to view direction and is unaffected by some important changes of clothing.