Dual Tracking Method for Real Time Object Tracking using Moving Camera

Shyam Lal,Rahul Kumar

doi:10.19026/rjaset.5.4643

Abstract

This study presents dual tracking method for real time object tracking using a moving camera. A real time object tracking using self aligning servo mechanism with webcam, dual tracking and effective localization of object is presented. The proposed dual tracking method works in two phases: In first phase tracking is done by joint color texture histogram with mean shift and in second phase tracking is done by servo setup. The proposed dual tracking method enjoys the benefit of double tracking feature, not only tracking but also to find out the coordinates of the tracking object which is of particular interest. The coordinates of a moving object enable us to estimates the real time location of the object which is helpful in surveillance and shooting purposes of suspected person in security area. The tracking of some specific objects in real life is of particular interest. Due to its enhanced automation the proposed dual tracking method can be applied in public security, surveillance, robotics and traffic control etc. The experimental results demonstrate that the proposed dual tracking method improves greatly the tracking area with accuracy and efficiency and also successfully find the coordinates of moving object.

Highlights

Object tracking is rapidly growing field in present scenario, there are two main reasons, first one is increasing number of suspicious persons, objects and complexity and second one is to make the things automatic in real life
The mean shift algorithm is successfully applied to object tracking and image segmentation (Comaniciu et al, 2003; Comaniciu and Meer, 2002).The mean shift tracking algorithm is an iterative kernel-based deterministic procedure which converges to a local maximum of the measurement function with certain assumptions based on the kernel behaviors
Target tracking with joint color-texture histogram: Local Binary Pattern (LBP): The Local binary pattern operator labels the pixel in an image by thresholding its neighborhood with the center value and considering the result as a binary number (Ojala et al, 2002, 2007)

Summary

Introduction

Object tracking is rapidly growing field in present scenario, there are two main reasons, first one is increasing number of suspicious persons, objects and complexity and second one is to make the things automatic in real life. At the computer the processing is done in two phases in first phase joint color texture histogram with means shift algorithm for object representation and in second phase proposed algorithm is used. In order to calculate the likelihood of the target model and the candidate model, a metric based on the Bhattacharyya coefficient is defined between the two normalized histograms pp(yy) and q q as follows (Ning et al, 2009): yy = ∑niin=h1 xxii wwii gg yy

Results

Conclusion