ÖN-EĞİTİLMİŞ DERİN SİNİR AĞLARI KULLANILARAK ULTRA GENİŞ BANT RADAR TABANLI EL HAREKETİ TANIMA

Abstract

Ultra geniş-bant (UGB) radar sensörleri insan hareketlerinin tanınmasında kritik bir öneme sahiptir. Sahip oldukları yüksek frekans özelliği ile hassas hareketlerin ayırt edilmesinde kullanılabilmektedirler. Ayrıca bu özellik, sensör boyutlarının portatif olmasına ve birçok alanda kolay kullanımına imkân tanır. Her ne kadar RGB kameralar üzerinden bu işlem yapılabilse de özellikle mahremiyet gizliliği konusunda problemler ile karşılaşılmaktadır. UGB sensörlerden elde edilen verilerde kişiler anonimleştiği için sadece hareket örüntüsü ele alınabilmektedir. Dolayısıyla, ev içi izleme ve yaşlı takibi gibi konularda önemli bir potansiyeli bulunmaktadır. Bu çalışmada, üç farklı sensör üzerinden toplanan radar verilerinden el hareketlerinin sınıflandırılması yapılmaktadır. Radar görselleri sağ, sol ve üst olacak şekilde arka arkaya bir araya getirilerek 3 kanallı bir görüntü elde edilmiştir. Ardından bu görüntüler üzerinden ön-eğitilmiş modeller eğitilmiş ve test edilmiştir. Eğitim ve test oranı 50:50 olduğu durumda %97.93 (DenseNet201) 75:25 oranında ise %97.65 (EfficientNetB0) oranında bir başarım elde edilmiştir. Tek sensör kullanımı yerine öne sürülen strateji ile modellerin eğitilmesinin önemli bir iyileştirme yaptığı ortaya koyulmuştur.

Keywords

• El Hareketi Tanıma
• Ultra Geniş-Bant
• Radar
• Derin Öğrenme
• Ön-eğitilmiş Model

ULTRA WIDEBAND RADAR-BASED HAND GESTURE RECOGNITION USING PRE-TRAINED DEEP NEURAL NETWORKS

Abstract

Ultra-wideband (UWB) radar sensors play a pivotal role in recognizing human movements. They can be used to distinguish sensitive movements with their high frequency feature. In addition, this feature allows the sensor dimensions to be portable and easy to use in many areas. Although this process can be done via RGB cameras, problems are encountered, especially regarding privacy. Since people are anonymized in the data obtained from UWB sensors, only their movement patterns can be considered. Therefore, it has significant potential in issues such as in-home and elderly monitoring. In this study, hand movements are classified from radar data collected through three different sensors. A 3-channel image was obtained by combining radar images one after the other, right, left, and top. Then, pre-trained models were trained and tested on these images. When the training and testing ratio was 50:50, a performance rate of 97.93% (DenseNet201) was achieved, and when the ratio was 75:25, a performance rate of 97.65% (EfficientNetB0) was achieved. It has been shown that training models with the proposed strategy instead of using a single sensor makes a significant improvement.

Keywords

• Hand Gesture Recognition
• Ultra Wide-Band
• RADAR
• Deep Learning
• Pre-trained Model

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