PMHT based multiple point targets tracking using multiple models in infrared image sequence

摘要

Data association and model selection are important factors for tracking multiple targets in a dense clutter environment. We propose a sequential probabilistic multiple hypotheses tracking (PMHT) based algorithm using interacting multiple modelling (IMM), namely the IMM-PMHT algorithm. Inclusion of IMM enables any arbitrary trajectory to be tracked without any a priori 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 measurement origin. It operates in an iterative mode using an expectation-maximization (EM) algorithm. The proposed algorithm uses only measurement association as missing data, which simplifies E-step and M-step. It is computationally more efficient, and an important characteristic of our proposed algorithm is that it operates in a single batch model, i.e. sequential, and hence can be used for real time tracking.