Araştırma Makalesi
BibTex RIS Kaynak Göster

Derin Öğrenme ile Kalabalık Analizi Üzerine Detaylı Bir Araştırma

Yıl 2018, Cilt: 11 Sayı: 3, 263 - 286, 31.07.2018
https://doi.org/10.17671/gazibtd.419205

Öz

Yapay
sinir ağları ve makine öğrenmesi, uzun yıllardır birçok problemin çözümünde kullanılmıştır.
Problemlerin ve modellerin karmaşıklaşması ve veri sayısındaki artış hesaplama
yükünü de beraberinde getirmiştir. Bu çalışmada yapay sinir ağlarından derin
öğrenmeye tüm geçiş süreci, modeller ve pratik uygulamalar kısa ve öz
gösterilmiştir. Ayrıca donanım, yazılım ve kullanılan kütüphaneler hakkında da
bilgiler verilmiştir. Özel olarak kalabalık analizi için kullanılan geleneksel
yöntemler özetlenmiştir. Kalabalık analizi için literatürdeki derin öğrenme
yaklaşımları detaylıca anlatılmış ve veri kümeleri tanıtılmıştır. Ayrıca son
yıllarda yapılmış çalışmalar analiz edilmiş ve karşılaştırılmıştır. Sonuç
olarak, kalabalık analizi, derin öğrenme yardımıyla başarılı sonuçlar alınan
hem akademik hem de pratik bir çalışma alanıdır.

Kaynakça

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A Comprehensive Survey of Deep Learning in Crowd Analysis

Yıl 2018, Cilt: 11 Sayı: 3, 263 - 286, 31.07.2018
https://doi.org/10.17671/gazibtd.419205

Öz

Artificial
neural networks and machine learning have been used to solve many problems for
decades. The complexity of the problems and models and the increase in the
number of data also brought with it the computation burden. In this study, the
whole transition process from artificial neural networks to deep learning,
models and applications are briefly demonstrated. Additionally information
about hardware, software, and used libraries is also provided. In particular,
canonical methods for crowd analysis have been summarized. Deep learning
approaches in the literature are pointed out in depth for crowd analysis and
datasets are overviewed. Furthermore, studies done in recent years have been
analyzed and compared. Consequently, crowd analysis is both an academic and a
practical field of study where successful results evaluation. As a result,
crowd analysis is both an academic and a practical field where fruitful results
are achieved with the help of deep learning.

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  • [109] V. Reddy, C. Sanderson ve B. C. Lovell, “Improved Anomaly Detection in Crowded Scenes via Cell-Based Analysis of Foreground Speed, Size and Textures”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp 55-61, 2011.
  • [110] V. Saligrama ve Z. Chen, “Video Anomaly Detection Based on Local Statistical Aggregates”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR’12), pp 2112-2119, 2012.
  • [111] L. Cao ve K. Huang, “Video-Based Crowd Density Estimation and Prediction System for Wide-Area Surveillance”, Future Video Technology, China Communications, Volume 10, No.5, pp. 79-88, 2013.
  • [112] A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar ve F-F. Li, “Large-Scale Video Classification with Convolutional Neural Networks”, In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (CVPR’14), pp 1725–1732, 2014.
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  • [115] H. Idrees, I. Saleemi, C. Seibert ve M. Shah, “Multi-Source Multi-Scale Counting in Extremely Dense Crowd Images”, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2547–2554, 2013.
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  • [117] J. Li, L. Huang ve C. Liu, “An Efficient Self-Learning People Counting System”, In First Asian Conference on Pattern Recognition (ACPR’11), pp 125-129, 2011.
  • [118] L. Boominathan, SS. S. Kruthiventi ve R. V. Babu, “Crowdnet: A Deep Convolutional Network for Dense Crowd Counting”, In Proceedings of the 2016 ACM on Multimedia Conference, pp 640–644, 2016.
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Toplam 141 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Bilgisayar Yazılımı
Bölüm Makaleler
Yazarlar

Merve Ayyüce Kızrak

Bülent Bolat Bu kişi benim

Yayımlanma Tarihi 31 Temmuz 2018
Gönderilme Tarihi 27 Nisan 2018
Yayımlandığı Sayı Yıl 2018 Cilt: 11 Sayı: 3

Kaynak Göster

APA Ayyüce Kızrak, M., & Bolat, B. (2018). Derin Öğrenme ile Kalabalık Analizi Üzerine Detaylı Bir Araştırma. Bilişim Teknolojileri Dergisi, 11(3), 263-286. https://doi.org/10.17671/gazibtd.419205

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