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Journals / Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji / 2017 / Cilt: 5 - Sayı: 1

Long Time Object Tracking Using Structural Features With Particle Filter

Yapısal Özellikleri Kullanan Parçacık Filtresi İle Uzun Süreli Nesne Takibi

Haluk DİLMEN
Muhammet Fatih TALU
Pages
107–118
Publication Date
2022-10-18
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Experimental results show that the proposed measurement model in object tracking increase classical tracking performance by at least %18.59

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Although long time tracking is an old research subject, it is still among the research subject actively attracting the attention of researchers and it is one of the research topic many studies conducted about.
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Key Findings & Data:Object tracking with particle filter, known to be among stochastic methods, models dynamics related to tracking subjects by taking advantage of state space variables, implemented in this study.
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Conclusion & Impact:Experimental results show that the proposed measurement model in object tracking increase classical tracking performance by at least %18.59
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

Although long time tracking is an old research subject, it is still among the research subject actively attracting the attention of researchers and it is one of the research topic many studies conducted about. Object tracking with particle filter, known to be among stochastic methods, models dynamics related to tracking subjects by taking advantage of state space variables, implemented in this study. Presenting improvements in the measurement models used to determine the weight of the particles, a new measurement model based on structural features of similarity coefficients used by SSIM with adaptive histogram equalization and weighting center of the object has been developed in The Particle Filter. Experimental results show that the proposed measurement model in object tracking increase classical tracking performance by at least %18.59

Özet

Nesnelerin uzun süreli takip edilmesi eski bir araştırma konusu olmasına rağmen araştırmacıların hala aktif olarak ilgisini çeken ve hakkında birçok çalışma yapılan araştırma konularının başında gelmektedir. Bu çalışmada tahminsel yöntemler arasında adı anılan, durum uzay değişkenlerinden yararlanarak takip konusunu ilgilendiren dinamikleri modelleyen parçacık filtresi ile nesne takibi gerçekleştirilmiştir. Parçacık filtresinde, parçacık ağırlıklarının belirlenmesinde kullanılan ölçüm modelinde yenilikler sunularak nesnenin yapısal özelliklerinin kullanıldığı SSIM benzerlik katsayısı ile birlikte adaptif histogram eşitlemesi ve nesne merkez bölgesinin ağırlıklandırılması temeline dayanan yeni bir ölçüm modeli geliştirilmiştir. Yapılan deneysel sonuçlar, önerilen nesne takip yönteminin klasik takip performansını en az %18.59 oranında arttırdığı gözlemlenmiştir

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Cite This Article

DİLMEN, H., TALU, M. F. (2022). Long Time Object Tracking Using Structural Features With Particle Filter. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, Cilt: 5 - Sayı: 1, 107–118. https://ajindex.com/articles/522193

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