Adaptive Multi-Strategy Observation of Kernelized Correlation Filter for Visual Object Tracking

Rif'At Ahdi Ramadhani, Grafika Jati, Wisnu Jatmiko, Ario Yudo Husodo

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

Abstract

Visual object tracking leads a vital role in multiple fields such as intelligent surveillance system, intelligent transportation system, human-computer interaction, behavior analysis, and intelligent driving assistance. In recent years, research of object tracking tends to focus on improving accuracy. Kernelized Correlation Filter (KCF) is considered as a baseline algorithm for real-time object tracking in term of high computation speed and accuracy by using correlation efficiently in the Frequency domain. However, correlation filter-based tracker is still prone to model drift due to incorrect predictions. This condition caused by varied appearance model especially in fast motion and motion blur. We proposed a new concept of KCF based tracker by adding confidence score scheme to detect tracker loss. Our tracker also introduces observation model with adaptive multi-strategy to find the lost target. We test the proposed method using OTB100 data that has strong characteristics in fast motion and motion blur. The result demonstrates that the proposed method was capable of recovering the lost target. The proposed tracker achieves better performance compared to the existing tracker in term of 0.887 in accuracy and 0.895 success rate.

Original languageEnglish
Title of host publication2019 4th Asia-Pacific Conference on Intelligent Robot Systems, ACIRS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages134-139
Number of pages6
ISBN (Electronic)9781728122298
DOIs
Publication statusPublished - Jul 2019
Event4th Asia-Pacific Conference on Intelligent Robot Systems, ACIRS 2019 - Nagoya, Japan
Duration: 13 Jul 201915 Jul 2019

Publication series

Name2019 4th Asia-Pacific Conference on Intelligent Robot Systems, ACIRS 2019

Conference

Conference4th Asia-Pacific Conference on Intelligent Robot Systems, ACIRS 2019
CountryJapan
CityNagoya
Period13/07/1915/07/19

Keywords

  • adaptive
  • correlation filter
  • multi-strategy
  • object tracking
  • observation model
  • tracking by detection

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    Ramadhani, RA. A., Jati, G., Jatmiko, W., & Husodo, A. Y. (2019). Adaptive Multi-Strategy Observation of Kernelized Correlation Filter for Visual Object Tracking. In 2019 4th Asia-Pacific Conference on Intelligent Robot Systems, ACIRS 2019 (pp. 134-139). [8936042] (2019 4th Asia-Pacific Conference on Intelligent Robot Systems, ACIRS 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACIRS.2019.8936042