An Improved Compressive Tracking Approach Using Multiple Random Feature Extraction Algorithm

Lan-Rong Dung*, Shih Chi Wang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


This paper presents an object-tracking algorithm with multiple randomly-generated features. We intent to improve the compressive tracking method whose results are fluctuated between good and bad. Because the compressive tracking method generates the image features randomly, the resulting image features varies from time to time. The object tracker might fail for missing some significant features. Therefore, the results of traditional compressive tracking are unstable. To solve the problem, the proposed approach generates multiple features randomly and chooses the best tracking results by measuring the similarity for each candidate. In this paper, we use the Bhattacharyya coefficient as the similarity measurement. The experimental results show that the proposed tracking algorithm can greatly reduce the tracking errors. The best performance improvements in terms of center location error, bounding box overlap ratio, and success rate are from 63.62 pixels to 15.45 pixels, from 31.75% to 64.48%, and from 38.51% to 82.58%, respectively.

Original languageEnglish
Title of host publicationAdvances in Computer Vision - Proceedings of the 2019 Computer Vision Conference CVC
EditorsKohei Arai, Supriya Kapoor
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783030177973
StateE-pub ahead of print - 24 Apr 2020
EventComputer Vision Conference, CVC 2019 - Las Vegas, United States
Duration: 25 Apr 201926 Apr 2019

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365


ConferenceComputer Vision Conference, CVC 2019
Country/TerritoryUnited States
CityLas Vegas


  • Compressive tracking
  • Feature extraction
  • Object tracking


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