Abstract
Postures can be identified and then classified from the video sequences using scale invariant keys as classification features and the results of classification can be used in various fields like surveillance, medical diagnosis and training purpose. In this paper frames are extracted from the given video files and transformed into a large collection of local feature vectors using scale invariant feature transform (SIFT), each of which is not affected by image translation, scaling and rotation, and to some extent invariant to illumination changes and affine or 3D projection. Features are grouped using k-means clustering in which each posture belongs to the cluster with the nearest mean. Multiclass support vector machine i.e., directed acyclic graph (DAGSVM) then assigns labels to the centres obtained from clustering. AdaBoost is incorporated to boost the performance accuracy of the classifier. Dataset used in this study is Bharatnatyam video dataset. The posture classification model is also shown to outperform state-of-art classification systems on videos as classification accuracy achieved using this frame work is 89%.
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