Çoklu Kamerada Gerçek Zamanlı Ölçeklenebilir Yüz Takibi Sistemi

Year 2024, EARLY VIEW, 1 - 1

Mehmet Fatih Ozdemır , Davut Hanbay

https://doi.org/10.2339/politeknik.1332952

Abstract

Yüz algılama ve izleme son yıllarda giderek daha popüler hale geldi. Günlük hayatta karşılaşılan güvenlik, savunma ve robotik uygulama kullanımlarında kritik öneme sahiptir. Bu amaçla yapay zeka ve makine öğrenmesi kullanılarak birçok karar destek veya uzman sistem geliştirilmiştir. Derin öğrenme ve donanım alanındaki gelişmeler sayesinde birçok etkin ve güvenilir yüz takip sistemi gerçekleştirilmiştir. Ancak hala çok az gerçek zamanlı ölçeklenebilir uçtan uca sistem var. Ayrıca, böyle bir sistemin birden çok kamerada gerçekleştirilmesi gerçek bir zorluktur. Bu çalışmada gerçek zamanlı, çok kameralı, derin öğrenme tabanlı yüz takip sistemi geliştirilmiştir. Gerçekleştirilen sistemde yüz tespiti için SCRFD modeli, yüz tanıma için ArcFace modeli ve daha kararlı yüz takibi için güncellenmiş DeepSORT algoritması kullanılmıştır. Ayrıca, çoklu kamera verilerini gerçek zamanlı ve ölçeklenebilir olarak işlemek için Apache Kafka akış işleme sistemi ve Socket.IO çift yönlü iletişim kütüphanesi kullanılmıştır. Önerilen sistemde girdi olarak görüntü verildiğinde yaklaşık 127 ms sonra web sayfasında görüntülenebilmektedir.

Keywords

Yüz Tanıma, Yüz Takibi, Derin Öğrenme, Çoklu Kamera

Supporting Institution

İnönü Üniversitesi Bilimsel Araştırmalar Proje Birimi

Project Number

YL-2021-2449

Real-Time Scalable System For Face Tracking In Multi-Camera

Abstract

Face detection and tracking have become increasingly popular in recent years. It has critical importance in security, defense, and robotics applications uses encountered in everyday life. For this purpose, many decision support or expert systems have been developed using artificial intelligence and machine learning. Thanks to the developments in the field of deep learning and hardware many effective and reliable face tracking systems have been realized. However there are still very few real-time scalable end-to-end systems. Also, the realization of this system on multiple cameras is a real challenge. In this study, a real-time, multi-camera, deep learning-based face tracking system has been developed. In the realized system, SCRFD model is used for face detection, ArcFace model is used for face recognition, and an updated DeepSORT algorithm is used for more stable face tracking. In addition, Apache Kafka stream processing system and Socket.IO bidirectional communication library were used to process multi-camera data in real-time and scalable. In the proposed system, when an image is input into the system, it can be displayed on the web page after approximately 127 ms

Keywords

Face Recognition, Face Tracking, Deep Learning, Multi-Camera

Project Number

YL-2021-2449

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