Enhanced Bayesian Tracker for Various Condition using Thermal Infrared Imagery

Umi Chasanah, Grafika Jati, Wisnu Jatmiko

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Self-driving car has the capability to navigate autonomously with high awareness of the surrounding environment. They have an intelligent method to avoid collision and predict movements of other objects. One of the key technology in order to achieve this level of safety is object tracking. Accurate and quick vision-based method compete to increase the reliability. Several tracking methods utilized visible spectrum camera to gather data. However, there are challenges that arise, especially in minimal lighting such as cloudy and rainy conditions or at night. To overcome this problem, we use thermal image. We propose a newly enhanced Smoothing Stochastic Approximate Monte Carlo (SSAMC) based tracker with unique preprocessing Gamma Normalization and Median Filter. We tested our tracker in self-driving car theme data from Linköping Thermal InfraRed (LTIR). This data is captured from both a moving and a static sensor, both have different difficulty level. The experiment results show that the tracker achieved a better result compared to other methods. We achieved an accuracy of 0.8786 with a higher frame per second computation time of 4.6405.

Original languageEnglish
Title of host publicationMHS 2018 - 2018 29th International Symposium on Micro-NanoMechatronics and Human Science
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538667927
DOIs
Publication statusPublished - Dec 2018
Event29th International Symposium on Micro-NanoMechatronics and Human Science, MHS 2018 - Nagoya, Japan
Duration: 10 Dec 201812 Dec 2018

Publication series

NameMHS 2018 - 2018 29th International Symposium on Micro-NanoMechatronics and Human Science

Conference

Conference29th International Symposium on Micro-NanoMechatronics and Human Science, MHS 2018
CountryJapan
CityNagoya
Period10/12/1812/12/18

Keywords

  • gamma normalization
  • median filter
  • self-driving car
  • SSAMC
  • thermal tracking

Fingerprint Dive into the research topics of 'Enhanced Bayesian Tracker for Various Condition using Thermal Infrared Imagery'. Together they form a unique fingerprint.

  • Cite this

    Chasanah, U., Jati, G., & Jatmiko, W. (2018). Enhanced Bayesian Tracker for Various Condition using Thermal Infrared Imagery. In MHS 2018 - 2018 29th International Symposium on Micro-NanoMechatronics and Human Science [8887042] (MHS 2018 - 2018 29th International Symposium on Micro-NanoMechatronics and Human Science). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MHS.2018.8887042