Browsing by Subject "Pose representation"

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Open Access
Multiple view human activity recognition
(2012) Pehlivan, Selen
This thesis explores the human activity recognition problem when multiple views are available. We follow two main directions: we first present a system that performs volume matching using constructed 3D volumes from calibrated cameras, then we present a flexible system based on frame matching directly using multiple views. We examine the multiple view systems compared to single view systems, and measure the performance improvements in recognition using more views by various experiments. Initial part of the thesis introduces compact representations for volumetric data gained through reconstruction. The video frames recorded by many cameras with significant overlap are fused by reconstruction, and the reconstructed volumes are used as substitutes of action poses. We propose new pose descriptors over these three dimensional volumes. Our first descriptor is based on the histogram of oriented cylinders in various sizes and orientations. We then propose another descriptor which is view-independent, and which does not require pose alignment. We show the importance of discriminative pose representations within simpler activity classification schemes. Activity recognition framework based on volume matching presents promising results compared to the state-of-the-art. Volume reconstruction is one natural approach for multi camera data fusion, but there can be few cameras with overlapping views. In the second part of the thesis, we introduce an architecture that is adaptable to various number of cameras and features. The system collects and fuses activity judgments from cameras using a voting scheme. The architecture requires no camera calibration. Performance generally improves when there are more cameras and more features; training and test cameras do not need to overlap; camera drop in or drop out is handled easily with little penalty. Experiments support the performance penalties, and advantages for using multiple views versus single view.
Open Access
A new pose-based representation for recognizing actions from multiple cameras
(Academic Press, 2011-02) Pehlivan, S.; Duygulu, P.
We address the problem of recognizing actions from arbitrary views for a multi-camera system. We argue that poses are important for understanding human actions and the strength of the pose representation affects the overall performance of the action recognition system. Based on this idea, we present a new view-independent representation for human poses. Assuming that the data is initially provided in the form of volumetric data, the volume of the human body is first divided into a sequence of horizontal layers, and then the intersections of the body segments with each layer are coded with enclosing circles. The circular features in all layers (i) the number of circles, (ii) the area of the outer circle, and (iii) the area of the inner circle are then used to generate a pose descriptor. The pose descriptors of all frames in an action sequence are further combined to generate corresponding motion descriptors. Action recognition is then performed with a simple nearest neighbor classifier. Experiments performed on the benchmark IXMAS multi-view dataset demonstrate that the performance of our method is comparable to the other methods in the literature. © 2010 Elsevier Inc. All rights reserved.