| dc.description.abstract | Gait classification is an effective and non-intrusive way for human identity identification on the research field of pattern recognition. The required device that we need is a camera, subjects just walk through the hallway naturally and the system can recognize their identity automatically. Recently, surveillance cameras are installed almost
everywhere. Gait classification can be used effectively in detecting illegal intruders without installing extra equipments. In this thesis, we adopt optical flow information as the basic features of gait. Three kinds of optical flow algorithms are manipulated on our proposed system and a variety of testing, analyses and discussions are made to highlight the experiment results. We test and verify that the optical flow information is really working not only on moving object detection and tracking but also on the complex problem of gait classification.
In our work, we first transform the input color image to gray level space. Then, employ optical flow algorithm to get the optical flow field. Some optical flow information on which intensity values are too small are removed and then Gaussian Model (GM) is employed to model the strong intensity information of optical flow. The locations of foreground objects (gait human) are extracted according to the mean and covariance of GM. After that, we adopt flows to construct feature histograms which belong to the bounding box. Since gait cycles are different with each subject, we apply the histogram matching method to normalize each video feature. Next, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are performed to reduce the feature dimension and find the best projection axis, respectively. Finally, k-Nearest Neighbor (k-NN) classifier is adopted to get the recognition result.
Through experimental analysis, we find that gait is a very effective feature to classify human’s identity even though the differences of walking postures between different persons are hard to observe. Although the accuracy of optical flow information is doubtful, we proved that optical flow information is useful for gait classification problem. In this thesis, we adopt optical flow information only and do not consider shape features or other information. Moreover, experimental results demonstrate that the
proposed framework contribute as good recognition rates as the contour-based approach in CASIA and our own database.
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