  In this paper, we address the basic problem of recognizing moving objects in
video images using Visual Vocabulary model and Bag of Words and track our
object of interest in the subsequent video frames using species inspired PSO.
Initially, the shadow free images are obtained by background modelling followed
by foreground modeling to extract the blobs of our object of interest.
Subsequently, we train a cubic SVM with human body datasets in accordance with
our domain of interest for recognition and tracking. During training, using the
principle of Bag of Words we extract necessary features of certain domains and
objects for classification. Subsequently, matching these feature sets with
those of the extracted object blobs that are obtained by subtracting the shadow
free background from the foreground, we detect successfully our object of
interest from the test domain. The performance of the classification by cubic
SVM is satisfactorily represented by confusion matrix and ROC curve reflecting
the accuracy of each module. After classification, our object of interest is
tracked in the test domain using species inspired PSO. By combining the
adaptive learning tools with the efficient classification of description, we
achieve optimum accuracy in recognition of the moving objects. We evaluate our
algorithm benchmark datasets: iLIDS, VIVID, Walking2, Woman. Comparative
analysis of our algorithm against the existing state-of-the-art trackers shows
very satisfactory and competitive results.
