Aerial Vehicle Tracking by Adaptive Fusion of Hyperspectral Likelihood Maps

TL;DR

This paper introduces a real-time hyperspectral likelihood map-based tracking method that adaptively fuses spectral data to improve target detection without extensive offline training or hyperparameter tuning.

Contribution

It presents a novel adaptive fusion approach for hyperspectral likelihood maps that enhances tracking performance and simplifies the detection process.

Findings

Outperforms existing fusion methods in tracking accuracy

Achieves comparable results to state-of-the-art hyperspectral tracking frameworks

Operates in real-time without extensive offline training

Abstract

Hyperspectral cameras can provide unique spectral signatures for consistently distinguishing materials that can be used to solve surveillance tasks. In this paper, we propose a novel real-time hyperspectral likelihood maps-aided tracking method (HLT) inspired by an adaptive hyperspectral sensor. A moving object tracking system generally consists of registration, object detection, and tracking modules. We focus on the target detection part and remove the necessity to build any offline classifiers and tune a large amount of hyperparameters, instead learning a generative target model in an online manner for hyperspectral channels ranging from visible to infrared wavelengths. The key idea is that, our adaptive fusion method can combine likelihood maps from multiple bands of hyperspectral imagery into one single more distinctive representation increasing the margin between mean value of foreground and background pixels in the fused map. Experimental results show that the HLT not only outperforms all established fusion methods but is on par with the current state-of-the-art hyperspectral target tracking frameworks.