Journals / Türk Doğa ve Fen Dergisi / 2017 / Cilt: 6 - Sayı: 2

Object detection and tracking methods: A comprehensive review

Nesne tespit ve takip metotları: Kapsamlı bir derleme

Pages
40–49
Publication Date
2017-12-16
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In this paper, current and widely used methods for object tracking are discussed and those methods were examined with their strengths and weaknesses.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Image processing is a method for reading, extracting, and processing important data in a digital image.
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Key Findings & Data:One of the fundamental part of the image processing is collecting knowledge acquisition about recognized objects and environments in the image like human vision system.
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Conclusion & Impact:In this paper, current and widely used methods for object tracking are discussed and those methods were examined with their strengths and weaknesses.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Nesne izleme · arka plan çıkarma · nesne algılama

Abstract

Image processing is a method for reading, extracting, and processing important data in a digital image. One of the fundamental part of the image processing is collecting knowledge acquisition about recognized objects and environments in the image like human vision system. Many methods are developed to meet the needs of detection, identification, classification and tracking of objects in images. Especially, finding the target object in the images and following this object in the future time periods is frequently used in many applications. One of the main problems which makes object tracking and analysis difficult is tracking the object in a changing environments. Several effective methods are developed in order to solve these kinds of problems. In this paper, current and widely used methods for object tracking are discussed and those methods were examined with their strengths and weaknesses.

Özet

Görüntü işleme dijital bir görüntü içerisindeki önemli bilgilerin okunması, çıkartılması ve işlenmesi için kullanılan bir yöntemdir. Görüntü içerisinde bulunan bir nesne ya da bir ortam hakkında insan görme sistemine benzer şekilde nitel bilgiler edinilmesi ve kullanılması görüntü işlemenin temel amaçlarındandır. Görüntülerde bulunan nesnelerin tespiti, tanımlanması, sınıflandırılması ve takibi gibi ihtiyaçları karşılayacak birçok yöntem geliştirilmiştir. Özellikle görüntülerdeki hedef nesnenin bulunması ve ileriki zaman dilimlerinde bu nesnenin kaybedilmemesi birçok alandaki uygulamalarda sıklıkla kullanılmaktadır. Takip edilecek nesnenin değişken bir ortam içinde bulunması nesne takibi ve analizini zorlaştıran temel problemdir. Bu problemleri çözmek ve nesnenin başarılı bir şekilde takip edilmesi için birçok farklı yöntem gelişmiştir. Bu çalışmada nesne takibi için güncel ve yaygın kullanılan yöntemler ele alınmıştır. İncelenen yöntemler güçlü/zayıf yönleri ile irdelenmiştir.

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Cite This Article

HANBAY, K., ÜZEN, H. (2017). Object detection and tracking methods: A comprehensive review. Türk Doğa ve Fen Dergisi, Cilt: 6 - Sayı: 2, 40–49. https://ajindex.com/articles/705763

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References (1)

  1. Hjelmås E., Kee L.B., Face Detection: A Survey,Computer vision and image understanding, 83, 236–274., 2001 [2] Anagnostopoulos C.-N.E., Anagnostopoulos I.E., Psoroulas I.D., Loumos, V., Kayafas, E., License Plate Recognition From Still Images and Video Sequences: A Survey,IEEE Transactions on intelligent transportation systems, 9, 3:377–391, 2008 [3] Balaji S.R., KarthikeyanS.,A survey on moving object tracking using image processing, 2017 11th International Conference on Intelligent Systems and Control (ISCO), IEEE, pp. 469–474, 2017 [4] Wang D., Unsupervised video segmentation based on watersheds and temporal tracking,IEEE Transactions on Circuits and Systems for video Technology, 8.5:539–546, 1998 [5] Chen Y., Yang X., Zhong B., Pan S., et al., CNNTracker: Online discriminative object tracking via deep convolutional neural network,Applied Soft Computing, 38:1088–1098, 2016 [6] Luo W., Xing J., Milan A., Zhang X., Multiple Object Tracking: A Literature Review,arXiv Prepr. arXiv1409.7618, 2014 [7] Risha K.P., Kumar A.C., Novel Method of Detecting Moving Object in Video,Procedia Technology, 24:1055–1060, 2016 [8] Avidan S., Support vector tracking,IEEE transactions on pattern analysis and machine intelligence, 26.8: 1064-1072, 2004 [9] Hinton G., Deng L., Yu D., Dahl G., Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups,IEEE Signal Processing Magazine, 29.6: 82-97, 2012 [10] Krizhevsky A., Sutskever I., Hinton G.E., ImageNet Classification with Deep Convolutional Neural Networks, In: Advances in neural information processing systems, p. 1097-1105,2012 [11] Szegedy C., Liu W., Jia Y., Sermanet P., Going Deeper With Convolutions, In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1–9, 2015 [12] Taigman, Y., Yang, M., Ranzato, M., Wolf, L., DeepFace: Closing the gap to human-level performance in face verification, In: Proceedings of the IEEE conference on computer vision and pattern recognition, p. 1701-1708, 2014 [13] Wu Y., Lim J., Yang M.-H., Online Object Tracking: A Benchmark, In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2411-2418, 2013 [14] Karasulu B., Videolardaki Hareketli Nesnelerin Tespit Ve Takibi İçin Uyarlanabilir Arkaplan Çıkarımı Yaklaşımı Tabanlı Bir Sistem,Uludağ Üniversitesi Mühendislik-Mimarlık Fakültesi Dergis, 18, 2013 [15] Shaikh S.H., Saeed K., Chaki N.,Moving Object Detection Approaches, Challenges