AdaBelief Optimizasyon Tekniğinin Derin Öğrenmeye Dayalı Yaya Rotası Tahmin Uygulamalarına Etkisinin “Yakınsama” açısından İncelenmesi

Year 2024, Volume: 6 Issue: 1, 1 - 10, 15.06.2024

Sevcan Turan , Feyzullah Temurtaş

https://doi.org/10.55213/kmujens.1418280

Abstract

Son yıllarda, görüntü işleme teknikleri kullanılarak yayaların takip edebileceği rotanın tahmini, hızla dikkat çeken bir araştırma konusu haline gelmiştir. Rota tahmin uygulamalarında Derin Öğrenmenin kullanımı, mühendislik çalışmalarıyla yapılan geleneksel parametre belirleme işlemlerine ihtiyaç duymayan ve daha doğru tahminler yapabilen yeni uygulamaların geliştirilmesini sağlamıştır. Rota tahmini için sıklıkla veriye dayalı olan gözetimli derin öğrenme modelleri kullanılmaktadır. Ancak, bu modellerin eğitimi yüksek hesaplama maliyeti getirmektedir. Bu maliyetleri azaltmak ve doğrulukları arttırmak için iyi yakınsama ve genelleştirme özelliklerine sahip optimizasyon yöntemlerini seçmek önemlidir. Bu çalışma, ETH/UCY veri kümeleri kullanılarak derin öğrenme mimarisi temelli geliştirilmiş rota tahmini algoritmalarının optimizasyon yöntemi açısından performansını incelemektedir. Özellikle, modelin eğitimi aşamasında yakınsama açısından AdaBelief optimizasyon tekniğinin avantajları ve dezavantajlarına odaklanılmaktadır. Çalışmanın sonuçları, AdaBelief’in eğitim sürecine pozitif bir katkıda bulunduğunu ve rota tahmini algoritmalarının genel performansını arttırabileceğini göstermektedir.

Keywords

AdaBelief, derin öğrenme, ETH/UCY, yaya rota tahmini, optimizasyon

Investigation of the Effects of AdaBelief Optimization Technique on Deep Learning-Based Pedestrian Path Prediction Applications in terms of “Convergence”

Abstract

In recent years, the prediction of pedestrian paths using computer vision techniques has become an increasingly attractive topic of research. The use of deep learning techniques has led to the development of new path prediction applications that do not rely on the traditional parameter determination processes with engineering studies. This has resulted in more accurate predictions. Supervised deep learning models, which are data-driven, have been widely used for path prediction. However, the training of these models is associated with high computational costs. To address this issue, it is important to choose optimization methods that have good convergence and generalization properties in order to reduce costs and improve accuracy. This study examines the performance of path prediction algorithms based on deep learning architectures using the ETH/UCY datasets. In particular, the study focuses on the advantages and disadvantages of the AdaBelief optimization technique in terms of convergence during the training phase. The results of the study show that the AdaBelief makes a positive contribution to the training process and can improve the overall performance of the path prediction algorithm.

Keywords

AdaBelief, deep learning, ETH/UCY, Pedestrian path prediction, optimization

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