Aerial Vehicle Tracking by Adaptive Fusion of Hyperspectral Likelihood Maps

Abstract

Hyperspectral cameras provide unique spectral signatures that can be used to solve surveillance tasks. This paper proposes a novel real-time hyperspectral likelihood maps-aided tracking method (HLT) inspired by an adaptive hyperspectral sensor. We focus on the target detection part of a tracking system and remove the necessity to build any offline classifiers and tune 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.

Tracking in Aerial Hyperspectral Videos Using Deep Kernelized Correlation Filters

“…This could be due to growing confusion as the larger ROIs contain a higher number of similar objects. Additionally, these results demonstrate the obvious need to couple the tracking-by-detection algorithms to a Multi-dimensional Assignment algorithm in a Bayes Filter framework in occlusion-dominated scenes [6], [5], [4].…”

“…The rich sensory information from hyperspectral imagery has been utilized by generative trackers [5], [6]. The discriminative and deep learning driven trackers on the other hand have recently improved the traditional object tracking dramatically.…”

“…This is proved in the previous section where we generated a large synthetic dataset representative of the WAMI platform and trained a convolutional network to classify real images from the WAMI platform. A hyperspectral tracking video representative of the RITMOS sensor was generated in previous studies [4], [6], [13], [10]. The hyperspectral tracking scenario has two different videos : (1) without trees and (2) with dense trees (25% full occlusion by trees).…”

“…The unique challenges posed by aerial platforms can be better addressed by smarter multi-modal data acquisition. In this direction, the Rochester Institute of Technology Multiobject Spectrometer (RITMOS) concept is utilized by a number of trackers [10], [11], [5], [6] as an example that can collect a small, targeted amount of hyperspectral data. The RITMOS captures data in two different modalities : (1) a full frame single channel image, and (2) limited hyperspectral data from the desired pixel locations.…”

Superpixel Contracted Graph-Based Learning for Hyperspectral Image Classification

“…This detailed spectral and spatial information increases the discriminative ability of HSIs compared to conventional colour images or multi-spectral images. As a result, hyperspectral imaging has been used in a wide range of applications including classification [1]- [3], object tracking [4]- [6], environmental monitoring [7], [8] and object detection [9]- [11].…”