Performance Evaluation of Camera-Based Time to Collision Calculation with Different Detectors&Descriptors

Abstract

Nowadays, the demand for producing and using autonomous vehicle is increasing. Due to the latest developments in technology, the capabilities of these vehicles in accident prevention are increasing. As a result of the accuracy of these capabilities, it is very important because it is human life. In today’s technology, the collision time calculation called TTC (Time to Collision) can be done in two different ways. The first of these methods is lidar-based calculation. In this paper TTC will be calculated using the camera-based method with different combinations of detectors and descriptors. Pros and cons of these methods will be discussed. The aim of this paper is to expose an exacting performance for related methods, especially its diverse combinations are used matching. In these experiments images are used for 10 images taken from real time traffic scenario of preceding vehicle. This paper includes seven methods for detectors and 6 methods for descriptors. These detectors and descriptors are used in 42 different combinations. The analysis includes four parameters such as total keypoint detection, total matches, total time in ms and performance ratio which is total matches divided by total time.

Keywords

• Autonomous Vehicles
• Image Processing
• Lidar
• Detector

Farklı Dedektörler ve Tanımlayıcılar ile Kamera Tabanlı Çarpışma Süresinin Hesaplanmasının Performans Değerlendirmesi

Öz

Günümüz otonom araç üretme ve kullanma talebi giderek artmaktadır. Teknolojideki gelişmeler nedeniyle bu araçların kaza önleme konusundaki yetenekleri de aynı oranda artmaktadır. Bu yeteneklerin doğruluğunun sonucu olarak insan hayatı söz konusu olduğunda oldukça önemlidir. Günümüz teknolojisinde TTC adı verilen çarpışma süresi hesabı iki farklı şekilde yapılabilmektedir. Bu yöntemden ilki lidar tabanlı hesaplamadır. Bu yazıda TTC, farklı dedektör ve tanımlayıcı kombinasyonları ile kamera tabanlı yöntem kullanılarak hesaplanacaktır. Bu bildirinin amacı, özellikle çeşitli kombinasyonların eşleştirilmesi için kullanılan yöntemler için hızlı. Bu deneylerde, öndeki aracın gerçek zamanlı trafik senaryosundan alınan 10 görüntü kullanılmıştır. Bu bildiri, dedektörler için yedi yöntem ve tanımlıyıcılar için 6 yöntem içermektedir. Bu dedektörler ve tanımlayıcılar 42 farklı kombinasyonda kullanılmaktadır. Analiz toplam anahtar nokta tespiti, toplam eşleşmeler, mili-saniye cinsinden toplam süre ve toplam eşleşmelerin toplam süreye bölünmesiyle elde edilen performans oranı gibi dört parametreyi içerir.

Anahtar Kelimeler

• Otonom Araçlar
• İmge İşleme
• Dedektör
• Tanımlayıcı

Kaynakça

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