Comparison of Optimization Algorithms for Multi-Object Tracking FairMOT Algorithm

Yıl 2021, Cilt: IDAP-2021 : 5th International Artificial Intelligence and Data Processing symposium Sayı: Special, 147 - 153, 20.10.2021

Mehmet Fatih Özdemir Ali Arı Davut Hanbay

https://doi.org/10.53070/bbd.990086

Öz

Many methods are used in the field of multi-object tracking today. Deep learning algorithms are among the most studied methods in this field. There are many parameters that need to be adapted in order to achieve high performance in deep learning based systems. One of the important parameters affecting performance in deep learning systems is the optimization algorithm used. In this study, Adam, RMSProp, Rprop, SGD optimization algorithms were compared for FairMOT algorithm. The MOT20 dataset was used when comparing the optimization algorithms. In the MOT20 validation dataset, overall highest accuracy value was obtained as 76.7% with the RMSprop optimization algorithm.

Anahtar Kelimeler

FairMOT, Multi-Object Tracking, Optimization Algorithms

Proje Numarası

FYL-2021-2449

Çoklu Nesne Takibi FairMOT Algoritması İçin Optimizasyon Algoritmalarının Karşılaştırılması

Öz

Çoklu nesne takibi alanında günümüzde birçok yöntem kullanılmaktadır. Derin öğrenme algoritmaları bu alanda en çok çalışılan yöntemler arasında yer almaktadır. Derin öğrenme tabanlı sistemlerde yüksek performans elde edilebilmesi için uyarlanması gereken birçok parametre vardır. Derin öğrenme sistemlerinde performansı etkileyen önemli parametrelerden birisi de kullanılan optimizasyon algoritmasıdır. Bu çalışmada FairMOT algoritması için Adam, RMSProp, Rprop, SGD optimizasyon algoritmaları karşılaştırılmıştır. Optimizasyon algoritmaları karşılaştırılırken MOT20 veri seti kullanılmıştır. MOT20 doğrulama veri setinde ortalama en yüksek doğruluk değeri, RMSprop optimizasyon algoritması ile %76.7 olarak elde edilmiştir.

Anahtar Kelimeler

Çoklu Nesne Takibi, Optimizasyon Algoritmaları, FairMOT

Destekleyen Kurum

İnönü Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi (BAP)

Proje Numarası

FYL-2021-2449

Teşekkür

Bu çalışma İnönü Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi (BAP) tarafından FYL-2021-2449 kodlu proje ile desteklenmiştir.

Kaynakça

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