Efficient integration of spectral features for vehicle tracking utilizing an adaptive sensor
Burak Uzkent, Matthew J. Hoffman, Anthony Vodacek · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2015
Object tracking in urban environments is an important and challenging problem that is traditionally tackled using visible and near infrared wavelengths. By inserting extended data such as spectral features of the objects one can improve the reliability of the identification process. However, huge increase in data created by hyperspectral imaging is usually prohibitive. To overcome the complexity problem, we propose a persistent air-to-ground target tracking system inspired by a state-of-the-art, adaptive, multi-modal sensor. The adaptive sensor is capable of providing panchromatic images as well as the spectra of desired pixels. This addresses the data challenge of hyperspectral tracking by only recording spectral data as needed. Spectral likelihoods are integrated into a data association algorithm in a Bayesian fashion to minimize the likelihood of misidentification. A framework for controlling spectral data collection is developed by incorporating motion segmentation information and prior information from a Gaussian Sum filter (GSF) movement predictions from a multi-model forecasting set. An intersection mask of the surveillance area is extracted from OpenStreetMap source and incorporated into the tracking algorithm to perform online refinement of multiple model set. The proposed system is tested using challenging and realistic scenarios generated in an adverse environment.