Paper
4 March 2015 Efficient integration of spectral features for vehicle tracking utilizing an adaptive sensor
Burak Uzkent, Matthew J. Hoffman, Anthony Vodacek
Author Affiliations +
Proceedings Volume 9407, Video Surveillance and Transportation Imaging Applications 2015; 940707 (2015) https://doi.org/10.1117/12.2082266
Event: SPIE/IS&T Electronic Imaging, 2015, San Francisco, California, United States
Abstract
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.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Burak Uzkent, Matthew J. Hoffman, and Anthony Vodacek "Efficient integration of spectral features for vehicle tracking utilizing an adaptive sensor", Proc. SPIE 9407, Video Surveillance and Transportation Imaging Applications 2015, 940707 (4 March 2015); https://doi.org/10.1117/12.2082266
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CITATIONS
Cited by 6 scholarly publications.
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KEYWORDS
Sensors

Detection and tracking algorithms

Kinematics

Motion models

Optical filters

Data modeling

Process modeling

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