Tracking and data association
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
PMHT Based Multiple Point Targets Tracking Using Multiple Models in Infrared Image Sequence
AVSS '03 Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance
Wavelet-Based Detection and Its Application to Tracking in an IR Sequence
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Estimation of radar cross section of a target under track
EURASIP Journal on Advances in Signal Processing
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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.