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

TL;DR

This paper presents a comprehensive method for recognizing and tracking moving objects in real-time video using a visual vocabulary model, Bag of Words, and a species-inspired PSO algorithm, achieving high accuracy and competitive results.

Contribution

It introduces a novel combination of visual vocabulary, Bag of Words, and species-inspired PSO for effective object recognition and tracking in video sequences.

Findings

High recognition accuracy demonstrated on benchmark datasets

Effective tracking with species-inspired PSO

Competitive performance against state-of-the-art methods

Abstract

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.