English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

Detection, Recognition and Tracking of Moving Objects from Real-time Video via Visual Vocabulary Model and Species Inspired PSO