Track Before Detect of Low SNR Objects in a Sequence of Image Frames Using Particle Filter

TL;DR

This paper presents a particle filter-based track-before-detect method for identifying and tracking low SNR objects in image sequences, effectively handling noise and clutter.

Contribution

It introduces a multiple model TBD particle filter approach that improves detection and tracking of low SNR objects in noisy, cluttered environments.

Findings

Effective detection in noisy conditions

Robust tracking of low SNR objects

Performance validated across various scenarios

Abstract

A multiple model track-before-detect (TBD) particle filter-based approach for detection and tracking of low signal to noise ratio (SNR) objects based on a sequence of image frames in the presence of noise and clutter is briefly studied in this letter. At each time instance after receiving a frame of image, first, some preprocessing approaches are applied to the image. Then, it is sent to the multiple model TBD particle filter for detection and tracking of an object. Performance of the approach is evaluated for detection and tracking of an object in different scenarios including noise and clutter.