Bayes Network Based Collaborating Control Algorithm in Active Multicamera Network with Applications to Object Tracking

Rui Zhao,Cairong Zhao,Duoqian Miao,Zhihua Wei,Yunpeng Wu

doi:10.1155/2014/219367

Abstract

Intelligent video surveillance network has many practical applications such as human tracking, vehicle tracking, and event detection. In this paper, an active multicamera network framework is designed for human detection and tracking by optimizing the cameras collaborating control. A multicamera collaborating control algorithm is proposed based on Bayes network to minimize the number of PTZ cameras with control and optimize the cameras' field of view. Hybrid human local feature transform selected by AdaBoost algorithm is adopted to improve the tracking precision. Experimental results on real world environment indicate the effectiveness and efficiency of proposed framework and algorithm.

Highlights

Monitoring and tracking mobile objects in public region such as subway stations, square, railway station, and commercial center are the most important function of surveillance [1,2,3]
PTZ cameras could change their angles to interest objects according to control instructions
It is a practical research to design intelligent camera network which could turn to interest objects automatically or semiautomatically according to control instructions in order to observe given targets

Summary

Introduction

Monitoring and tracking mobile objects in public region such as subway stations, square, railway station, and commercial center are the most important function of surveillance [1,2,3]. In [23], a principled Partially Observable Markov Decision Process-based approach was proposed to coordinate and control a network of active cameras for tracking and observing multiple mobile objects. These works help to eliminate the dependency on FOV of cameras for tracking the objects’ locations by exploiting probability framework to model the multicamera network. We propose an algorithm for real world video surveillance with PTZ cameras, where the multicamera collaboration scheme is constructed based on Bayes Network by taking into account the GIS information and camera action into models.

Proposed System Model

Multicamera Collaborative Tracking Based on Bayes Network

Experiments and Discussions

Conclusion and Future Works