عنوان
پدید آورنده
موضوع
رده
کتابخانه
محل استقرار
شماره کتابشناسی ملی
شماره
TL51773
زبان اثر
زبان متن نوشتاري يا گفتاري و مانند آن
انگلیسی
عنوان و نام پديدآور
عنوان اصلي
An Experimental Evaluation of Probabilistic Deep Networks for Real-time Traffic Scene Representation using Graphical Processing Units
نام عام مواد
[Thesis]
نام نخستين پديدآور
El-Shaer, Mennat Allah Ahmed Mohammed
نام ساير پديدآوران
Ozguner, Fusun
وضعیت نشر و پخش و غیره
نام ناشر، پخش کننده و غيره
The Ohio State University
تاریخ نشرو بخش و غیره
2019
يادداشت کلی
متن يادداشت
107 p.
یادداشتهای مربوط به پایان نامه ها
جزئيات پايان نامه و نوع درجه آن
Ph.D.
کسي که مدرک را اعطا کرده
The Ohio State University
امتياز متن
2019
یادداشتهای مربوط به خلاصه یا چکیده
متن يادداشت
The problem of scene understanding and environment perception has been an important one in robotics research, however existing solutions applied in current Advanced Driving Assistance systems (ADAS) are not robust enough to ensure the safety of traffic participants. ADAS development begins with sensor data collection and algorithms that can interpret that data to guide the intelligent vehicle's control decisions. Much work has been done to extract information from camera based image sensors, however most solutions require hand-designed features that usually break down under different lighting and weather conditions. Urban traffic scenes, in particular, present a challenge to vision perception systems due to the dynamic interactions among participants whether they are pedestrians, bicyclists, or other vehicles. Object detection deep learning models have proved successful in classifying or identifying objects on the road, but do not allow for the probabilistic reasoning and learning that traffic situations require. Deep Generative Models that learn the data distribution of training sets are capable of generating samples from the trained model that better represent sensory data, which leads to better feature representations and eventually better perception systems. Learning such models is computationally intensive so we decide to utilize Graphics Processing chips designed for vision processing. In this thesis, we present a small image dataset collected from different types of busy intersections on a university campus along with our CUDA implementations of training a Restricted Boltzmann Machine on NVIDIA GTX1080 GPU, and its generative sampling inference on an NVIDIA Tegra X1 SoC module. We demonstrate the sampling capability of a simple unsupervised network trained on a subset of the dataset, along with proling results from experiments done on the Jetson TX1 platform. We also include a quantitative study of different GPU optimization techniques performed on the Jetson TX1.
اصطلاحهای موضوعی کنترل نشده
اصطلاح موضوعی
Artificial intelligence
اصطلاح موضوعی
Computer engineering
اصطلاح موضوعی
Computer science
اصطلاح موضوعی
Electrical engineering
اصطلاح موضوعی
Robotics
نام شخص به منزله سر شناسه - (مسئولیت معنوی درجه اول )
مستند نام اشخاص تاييد نشده
El-Shaer, Mennat Allah Ahmed Mohammed
نام شخص - ( مسئولیت معنوی درجه دوم )
مستند نام اشخاص تاييد نشده
Ozguner, Fusun
شناسه افزوده (تنالگان)
مستند نام تنالگان تاييد نشده
The Ohio State University
دسترسی و محل الکترونیکی
نام الکترونيکي
مطالعه متن کتاب وضعیت انتشار
فرمت انتشار
p
اطلاعات رکورد کتابشناسی
نوع ماده
[Thesis]
کد کاربرگه
276903
اطلاعات دسترسی رکورد
سطح دسترسي
a
تكميل شده
Y
