A Hierarchical Estimator for Object Tracking

Chin-Wen Wu,Pau-Choo Chung,Yi-Nung Chung

doi:10.1155/2010/592960

Abstract

A closed-loop local-global integrated hierarchical estimator (CLGIHE) approach for object tracking using multiple cameras is proposed. The Kalman filter is used in both the local and global estimates. In contrast to existing approaches where the local and global estimations are performed independently, the proposed approach combines local and global estimates into one for mutual compensation. Consequently, the Kalman-filter-based data fusion optimally adjusts the fusion gain based on environment conditions derived from each local estimator. The global estimation outputs are included in the local estimation process. Closed-loop mutual compensation between the local and global estimations is thus achieved to obtain higher tracking accuracy. A set of image sequences from multiple views are applied to evaluate performance. Computer simulation and experimental results indicate that the proposed approach successfully tracks objects.

Highlights

Visual object tracking is an important issue in computer vision
In contrast to existing approaches, the present study proposes a closed-loop local-global integrated hierarchical estimator (CLGIHE) for object tracking using multiple cameras
The following nomenclature is used throughout this study: xi denotes local estimate, “ ” denotes estimate, “super T” denote transpose, “−” denotes the a priori estimate, “+” denotes the a posteriori estimate, pi denotes the local covariance matrix, ki denotes the Kalman gain of the local estimate, X, P, and K denote the global estimate, the covariance matrix, and the Kalman gain, respectively, In denotes an n×n identity matrix, and n denotes the dimension of state vector xi

Summary

Introduction

Visual object tracking is an important issue in computer vision. It has applications in many fields, including visual surveillance, human behavior analysis, maneuvering target tracking, and traffic monitoring. X+(k), P+(k) Figure 1: Proposed Kalman-filter-based hierarchical estimator for object tracking. A particle-based framework for single-object tracking with occlusions in a camera network This approach requires prior knowledge of the environment and the FOV of each camera for estimating the likelihood of whether the object will be occluded from the view of a camera. Medeiros et al [26] proposed a cluster-based Kalman filter algorithm for a wireless camera (sensor) network for object tracking. In contrast to existing approaches, the present study proposes a closed-loop local-global integrated hierarchical estimator (CLGIHE) for object tracking using multiple cameras. The Kalman filter is used to combine the local and global estimates into one estimate for mutual compensation since it can be efficiently integrated into a hierarchical fusion algorithm.

System Overview

Proposed Hierarchical Estimator for Object Tracking

View-1

Method

Experimental Results

Conclusion