Matrix Separation and Poisson Multi-Bernoulli Mixture Filtering for Extended Multi-Target Tracking with Infrared Images

Abstract

Multi-target tracking using infrared images is receiving more and more attention. There are many state-of-the-art methods, and the deep learning network and low-rank and sparse matrix separation are two kinds of methods with high accuracy. However, the former suffers from heavy training samples, and the latter requires high-dimensional processing, meaning its computing cost is huge. In this work, a united detection and tracking method with matrix separation and PMBM filtering is proposed. In the detection process, a low-rank and sparse matrix separation algorithm with a differentiable form based on a single image is constructed. In the filtering process, the multi-target state is modeled as a PMBM distribution, which is conjugate in the Bayesian framework. The two processes interact mutually in that the detection provides measurements, and the filtering offers prior information for the next detection to improve accuracy. The computational complexity is given by a theoretical analysis, which shows a significant reduction. The numerical analysis, carried out on a practical dataset, verifies an enhancement in the BSF and SCRG metrics and ROC curves.