A Review On Object Detection And Tracking With Deep Learning Techniques

Year 2021, Issue: 25, 159 - 171, 31.08.2021

Fatma Gülşah Tan Asım Sinan Yüksel Erdal Aydemir Mevlüt Ersoy

https://doi.org/10.31590/ejosat.878552

Cited By: 7

Abstract

Deep learning is one of the artificial intelligence approaches that has recently become popular for minimizing human error. Deep learning techniques have the ability to successfully detect and interpret with the use of large amounts of data in many areas. Especially, the rapid increase in labeled data accumulated in the field of image processing has made it necessary to turn to deep learning algorithms. With the increasing data in these areas, deep learning methods are used to separate useful information from big data and to give meaning to text, images and audio files. In recent years, there has been an increase in the studies conducted in the field of object detection and object tracking. If there is an object to be followed after detection and analysis on non-stationary images such as videos, it is more difficult to extract meaningful information. In such cases, the use of deep learning algorithms enables image processing problems to be solved easily. The aim of this study is to examine the applications of deep learning and object detection and tracking, to explain the latest developments, to help researchers who will work in this field by giving information about popular libraries, data sets, algorithms.

Keywords

Deep Learning, Image Processing, Object Detection, Object Tracking, Neural Networks

Derin Öğrenme Teknikleri İle Nesne Tespiti Ve Takibi Üzerine Bir İnceleme

Abstract

Derin öğrenme, son zamanlarda insan hatalarını en aza indirmesiyle popüler olan yapay zekâ yaklaşımlarındandır. Derin öğrenme teknikleri birçok alanda büyük miktardaki veri kullanımı ile başarılı bir şekilde algılama, yorumlama yapabilme yeteneğine sahiptir. Özellikle görüntü işleme alanında birikmiş etiketli verilerdeki hızlı artış derin öğrenme algoritmalarına yönelmeyi zorunlu hale getirmiştir. Bu alanlardaki verilerin giderek artmasıyla büyük verilerden yararlı bilgiyi ayırmak ve metin, görüntü, ses dosyalarına anlam kazandırmak amacıyla derin öğrenme yöntemleri kullanılmaktadır. Son yıllarda, nesne tespiti ve nesne takibi alanında yapılan çalışmalarda artış görülmektedir. Videolar gibi durağan olmayan görüntüler üzerinde tespit ve analiz sonrasında takip edilecek olan bir nesne varsa anlamlı bilgiler çıkarmak daha zor olmaktadır. Bu gibi durumlarda derin öğrenme algoritmalarının kullanılması görüntü işleme problemlerinin kolaylıkla çözüme kavuşturulabilmesini sağlamaktadır. Bu çalışmanın amacı; derin öğrenme ile nesne tespiti ve takibi konusunda yapılan uygulamaları incelemek, son gelişmeleri anlatmak, popüler kütüphaneler, veri setleri, algoritmalar hakkında bilgi vererek bu alanda çalışacak olan araştırmacılara yardımcı olmaktır.

Keywords

Derin Öğrenme, Görüntü İşleme, Nesne Tespiti, Nesne Takibi, Sinir Ağları

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