Image and Video Indexing Using Networks of Operators
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Research Article A Combined PMHT and IMM Approach to Multiple-Point Target Tracking in Infrared Image Sequence Mukesh A. Zaveri,1 S. N. Merchant,2 and Uday B. Desai2 1 Computer 2 SPANN
Engineering Department, Sardar Vallabhbhai National Institute of Technology, Surat 395007, India Laboratory, Electrical Engineering Department, Indian Institute of Technology-Bombay, Powai, Mumbai 400076, India
Received 18 August 2006; Revised 28 April 2007; Accepted 30 July 2007 Recommended by Ferran Marques 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. Copyright © 2007 Mukesh A. Zaveri et al. 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.
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INTRODUCTION
Tracking of multiple moving targets in the presence of clutter has significance in surveillance, navigation, and military application. Various approaches have been proposed for multitarget tracking [1, 2]. The most popular filter used for tracking is the Kalman filter [3–9] because of its simplicity and since it is optimal estimate with linear and Gaussian model assumptions. The performance of a tracking algorithm depends on the data association method used for the observation to track assignment and the model selected to track the movement of a target. For data association, the most common method used is the nearest neighbor (NN) method [1]. The performance of the NN-based data association method degrades in a dense clutter environment. To avoid uncertainty about the origin of observation, joint probabilistic data association filter (JPDAF) and multiple hypothesis tracking (MHT) schemes have been developed [1]. In both these cases, the complexity of the algorithm increases with the increase in the number of observations and the number of targets, as both techniques involve formation and evaluation of all the possible data association events. Maximum likelihood approach and PMHT algorithm have been proposed [10–12], which reduces the complexity. Var-
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