Real-Time Human Detection for Aerial Captured Video Sequences via Deep Models
المؤلفون المشاركون
AlDahoul, Nouar
Md Sabri, Aznul Qalid
Mansoor, Ali Mohammed
المصدر
Computational Intelligence and Neuroscience
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-14، 14ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-02-12
دولة النشر
مصر
عدد الصفحات
14
التخصصات الرئيسية
الملخص EN
Human detection in videos plays an important role in various real life applications.
Most of traditional approaches depend on utilizing handcrafted features which are problem-dependent and optimal for specific tasks.
Moreover, they are highly susceptible to dynamical events such as illumination changes, camera jitter, and variations in object sizes.
On the other hand, the proposed feature learning approaches are cheaper and easier because highly abstract and discriminative features can be produced automatically without the need of expert knowledge.
In this paper, we utilize automatic feature learning methods which combine optical flow and three different deep models (i.e., supervised convolutional neural network (S-CNN), pretrained CNN feature extractor, and hierarchical extreme learning machine) for human detection in videos captured using a nonstatic camera on an aerial platform with varying altitudes.
The models are trained and tested on the publicly available and highly challenging UCF-ARG aerial dataset.
The comparison between these models in terms of training, testing accuracy, and learning speed is analyzed.
The performance evaluation considers five human actions (digging, waving, throwing, walking, and running).
Experimental results demonstrated that the proposed methods are successful for human detection task.
Pretrained CNN produces an average accuracy of 98.09%.
S-CNN produces an average accuracy of 95.6% with soft-max and 91.7% with Support Vector Machines (SVM).
H-ELM has an average accuracy of 95.9%.
Using a normal Central Processing Unit (CPU), H-ELM’s training time takes 445 seconds.
Learning in S-CNN takes 770 seconds with a high performance Graphical Processing Unit (GPU).
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
AlDahoul, Nouar& Md Sabri, Aznul Qalid& Mansoor, Ali Mohammed. 2018. Real-Time Human Detection for Aerial Captured Video Sequences via Deep Models. Computational Intelligence and Neuroscience،Vol. 2018, no. 2018, pp.1-14.
https://search.emarefa.net/detail/BIM-1130598
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
AlDahoul, Nouar…[et al.]. Real-Time Human Detection for Aerial Captured Video Sequences via Deep Models. Computational Intelligence and Neuroscience No. 2018 (2018), pp.1-14.
https://search.emarefa.net/detail/BIM-1130598
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
AlDahoul, Nouar& Md Sabri, Aznul Qalid& Mansoor, Ali Mohammed. Real-Time Human Detection for Aerial Captured Video Sequences via Deep Models. Computational Intelligence and Neuroscience. 2018. Vol. 2018, no. 2018, pp.1-14.
https://search.emarefa.net/detail/BIM-1130598
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1130598
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر