Open Access

A Combined PMHT and IMM Approach to Multiple-Point Target Tracking in Infrared Image Sequence

EURASIP Journal on Image and Video Processing20072007:019139

DOI: 10.1155/2007/19139

Received: 18 August 2006

Accepted: 30 July 2007

Published: 10 September 2007

Abstract

Data association and model selection are important factors for tracking multiple targets in a dense clutter environment. In this paper, we provide an effective solution to the tracking of multiple single-pixel maneuvering targets in a sequence of infrared images by developing an algorithm that combines a sequential probabilistic multiple hypothesis tracking (PMHT) and interacting multiple model (IMM). We explicitly model maneuver as a change in the target's motion model and demonstrate its effectiveness in our tracking application discussed in this paper. We show that inclusion of IMM enables tracking of any arbitrary trajectory in a sequence of infrared images without any a priori special information about the target dynamics. IMM allows us to incorporate different dynamic models for the targets and PMHT helps to avoid the uncertainty about the observation origin. It operates in an iterative mode using expectation-maximization (EM) algorithm. The proposed algorithm uses observation association as missing data.

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Authors’ Affiliations

(1)
Computer Engineering Department, Sardar Vallabhbhai National Institute of Technology
(2)
SPANN Laboratory, Electrical Engineering Department, Indian Institute of Technology-Bombay

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Copyright

© Mukesh A. Zaveri et al. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.