Integrate Occlusion Control Block to Your Tracker

Kazımhan Peker*, Deniz Turgay Altılar

*Corresponding author for this work

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

Abstract

Visual object tracking is a hot topic in computer vision, focusing on localization of target object and size of a target object across successive frames. However, this endeavor faces a couple of challenges, including occlusion, illumination, scale variations, background clutter, and detecting small objects. In these challenges, we focus on occlusion which occurs when other objects partially or fully conceal the target object. This study presents the implementation of the Occlusion Control Block (OCB), which is divided into two sub-blocks: the Occlusion Awareness Block (OAB) and the Estimation Block (EB). The Occlusion Awareness Block (OAB) is integrated with object detection, object size control, target object foreground detection, object velocity control, and the calculation of occlusion duration. EB estimates the target object location sequential frames under occlusion scenario. OCB is integrated to OSTRACK object tracker model as an occlusion handler block, which is checked against other object tracing algorithms through the experiments with LaSOT, LaSOT(Ext), TrackingNet, and GOT10k. The results demonstrate that the integration of our novel OCB design significantly enhances the occlusion handling performance of the base object tracking model. Additionally, the OCB block does not require any dataset training and can be easily integrated into any object tracking algorithm.

Original languageEnglish
Title of host publicationIntelligent Systems and Applications - Proceedings of the 2025 Intelligent Systems Conference IntelliSys
EditorsKohei Arai
PublisherSpringer Science and Business Media Deutschland GmbH
Pages280-293
Number of pages14
ISBN (Print)9783031999642
DOIs
Publication statusPublished - 2025
Event11th Intelligent Systems Conference, IntelliSys 2025 - Amsterdam, Netherlands
Duration: 28 Aug 202529 Aug 2025

Publication series

NameLecture Notes in Networks and Systems
Volume1554 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference11th Intelligent Systems Conference, IntelliSys 2025
Country/TerritoryNetherlands
CityAmsterdam
Period28/08/2529/08/25

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Keywords

  • Computer vision
  • Object tracking
  • Occlusion

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