Model Based Design of Video Tracking Based on MATLAB/Simulink and DSP

Chachou Mohamed Yacine,Zhiwen Liu,Zhiguo Zhou

doi:10.19026/rjaset.7.748

Abstract

The implementation of digital image processing on electronic boards is a current problem. In this study, we present a Model-Based Design of video tracking based on Matlab/Simulink and DSP. The implementation on DSP, of multi-objects detection and tracking algorithms of two kinds of applications inside and outside, is obtained by using automatic code generation that is code composer studio. The transmission and reception of data is realized by a network connection via Ethernet port between DSP and PC. This allows us, in the future, to extend the number of DSP working in parallel and their IP addresses would be generated by a DHCP server.

Highlights

With recent advances of computer technology automated visual surveillance has become a popular area for research and development
The video tracking system consists of the hardware system and the software development environment
We found that the detection and the tracking are made well

Summary

INTRODUCTION

With recent advances of computer technology automated visual surveillance has become a popular area for research and development. The objective of this study is to do a model-based design of multi-objects video detection and tracking of the indoor and the outdoor applications. (d) Slave block diagram, outdoor Fig. 1: Model-based design video tracking, indoor and outdoor applications (a). Indoor application: Because we have chosen a stationary background method to build the referential image, we first send N-samples enable (N equal to 20) to the DSP (Fig. 1a and Fig. 2a) In this case, unlike to the case of outdoor environment, the reference image can be taken when the scene is empty. Outdoor application: In contrast to the indoor application, in this case we should choose the adaptive background method to build the referential image, because we have no idea about the scene, that’s why, we just need to sent alone, the input video to the DSP processor (Fig. 1c and Fig. 2b). The projection equations for the state estimation and for the state covariance matrix are as following: ቊP୩ିାଵxො=୩ିାΦଵ ୩=P୩ΦΦ୩୩୘xො୩+ Q୩

IMPLEMENTATION AND RESULTS

Findings

CONCLUSION