Multiple-kernel adaptive segmentation and tracking (MAST) for robust object tracking

Zheng Tang, Jenq Neng Hwang, Yen Shuo Lin, Jen-Hui Chuang

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

17 Scopus citations

Abstract

In a video surveillance system with static cameras, object segmentation often fails when part of the object has similar color with the background, resulting in poor performance of the subsequent object tracking. Multiple kernels have been utilized in object tracking to deal with occlusion, but the performance still highly depends on segmentation. This paper presents an innovative system, named Multiple-kernel Adaptive Segmentation and Tracking (MAST), which dynamically controls the decision thresholds of background subtraction and shadow removal around the adaptive kernel regions based on the preliminary tracking results. Then the objects are tracked for the second time according to the adaptively segmented foreground. Evaluations of both segmentation and tracking on benchmark datasets and our own recorded video sequences demonstrate that the proposed method can successfully track objects in similar-color background and/or shadow areas with favorable segmentation performance.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1115-1119
Number of pages5
ISBN (Electronic)9781479999880
DOIs
StatePublished - 18 May 2016
Event41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai, China
Duration: 20 Mar 201625 Mar 2016

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2016-May
ISSN (Print)1520-6149

Conference

Conference41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
Country/TerritoryChina
CityShanghai
Period20/03/1625/03/16

Keywords

  • Adaptive Segmentation
  • Background Subtraction
  • Multiple Kernels
  • Object Tracking
  • Shadow Removal

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