Moving Target Detection and Active Tracking with a Multicamera Network

Long Zhao,Tie Jun Li,Qing Yun Wang,Li Fei Liu,Jian Hua Zhou

doi:10.1155/2014/976574

Abstract

We propose a systematic framework for Intelligence Video Surveillance System (IVSS) with a multicamera network. The proposed framework consists of low-cost static and PTZ cameras, target detection and tracking algorithms, and a low-cost PTZ camera feedback control algorithm based on target information. The target detection and tracking is realized by fixed cameras using a moving target detection and tracking algorithm; the PTZ camera is manoeuvred to actively track the target from the tracking results of the static camera. The experiments are carried out using practical surveillance system data, and the experimental results show that the systematic framework and algorithms presented in this paper are efficient.

Highlights

Moving target detection and tracking has a variety of applications in the field of computer vision, such as intelligence video surveillance, motion analysis, action recognition, environmental monitoring, and disaster response
The target detection and tracking is done by static cameras using a moving target detection and tracking algorithm; the target of interest is actively tracked by the PTZ cameras using a simple feedback control strategy
The system is composed of the low-cost static and PTZ cameras, the target detection and tracking algorithms, and the low-cost PTZ camera feedback control algorithm based on target information

Summary

Introduction

Moving target detection and tracking has a variety of applications in the field of computer vision, such as intelligence video surveillance, motion analysis, action recognition, environmental monitoring, and disaster response. To detect the moving targets from video frames of the static cameras, one of the widely used algorithms is background subtraction approach [12, 13]. Since its sensitivity cannot be accurately tuned, its ability to successfully handle both high- and low-frequency changes in the background is debatable To overcome these shortages, sample-based techniques [17] circumvent a part of the parameter estimation step by building their models from observed pixel values and enhance their robustness to noise. The contribution of this paper lies in that we design the real time control strategy of active cameras based on the target information obtained by detection and tracking algorithms.

System Framework and Problem Statement

Multicamera Target Tracking and PTZ Camera Control

Experimental Test

Conclusion and Future Work