Confidence-Guided Frame Skipping to Enhance Object Tracking Speed

Object tracking is a challenging task in computer vision. While simple tracking methods offer fast speeds, they often fail to track targets. To address this issue, traditional methods typically rely on complex algorithms. This study presents a novel approach to enhance object tracking speed via conf...

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Main Author: Yun Gu Lee
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/24/24/8120
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author Yun Gu Lee
author_facet Yun Gu Lee
author_sort Yun Gu Lee
collection DOAJ
description Object tracking is a challenging task in computer vision. While simple tracking methods offer fast speeds, they often fail to track targets. To address this issue, traditional methods typically rely on complex algorithms. This study presents a novel approach to enhance object tracking speed via confidence-guided frame skipping. The proposed method is strategically designed to complement existing methods. Initially, lightweight tracking is used to track a target. Only in scenarios where it fails to track is an existing, robust but complex algorithm used. The contribution of this study lies in the proposed confidence assessment of the lightweight tracking’s results. The proposed method determines the need for intervention by the robust algorithm based on the predicted confidence level. This two-tiered approach significantly enhances tracking speed by leveraging the lightweight method for straightforward situations and the robust algorithm for challenging scenarios. Experimental results demonstrate the effectiveness of the proposed approach in enhancing tracking speed.
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spelling doaj-art-9c2ee6628d1f454898fad440ffb527f42025-08-20T02:01:23ZengMDPI AGSensors1424-82202024-12-012424812010.3390/s24248120Confidence-Guided Frame Skipping to Enhance Object Tracking SpeedYun Gu Lee0School of Software, Kwangwoon University, Kwangwoon-ro 20, Nowon-gu, Seoul 01897, Republic of KoreaObject tracking is a challenging task in computer vision. While simple tracking methods offer fast speeds, they often fail to track targets. To address this issue, traditional methods typically rely on complex algorithms. This study presents a novel approach to enhance object tracking speed via confidence-guided frame skipping. The proposed method is strategically designed to complement existing methods. Initially, lightweight tracking is used to track a target. Only in scenarios where it fails to track is an existing, robust but complex algorithm used. The contribution of this study lies in the proposed confidence assessment of the lightweight tracking’s results. The proposed method determines the need for intervention by the robust algorithm based on the predicted confidence level. This two-tiered approach significantly enhances tracking speed by leveraging the lightweight method for straightforward situations and the robust algorithm for challenging scenarios. Experimental results demonstrate the effectiveness of the proposed approach in enhancing tracking speed.https://www.mdpi.com/1424-8220/24/24/8120visual trackingobject trackingfast object trackingonline tracking
spellingShingle Yun Gu Lee
Confidence-Guided Frame Skipping to Enhance Object Tracking Speed
Sensors
visual tracking
object tracking
fast object tracking
online tracking
title Confidence-Guided Frame Skipping to Enhance Object Tracking Speed
title_full Confidence-Guided Frame Skipping to Enhance Object Tracking Speed
title_fullStr Confidence-Guided Frame Skipping to Enhance Object Tracking Speed
title_full_unstemmed Confidence-Guided Frame Skipping to Enhance Object Tracking Speed
title_short Confidence-Guided Frame Skipping to Enhance Object Tracking Speed
title_sort confidence guided frame skipping to enhance object tracking speed
topic visual tracking
object tracking
fast object tracking
online tracking
url https://www.mdpi.com/1424-8220/24/24/8120
work_keys_str_mv AT yungulee confidenceguidedframeskippingtoenhanceobjecttrackingspeed