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دسته بندی:
بینایی ماشین - Machine vision
سال انتشار:
2017
عنوان انگلیسی مقاله:
Training My Car to See using Virtual Worlds
ترجمه فارسی عنوان مقاله:
آموزش اتومبیل من برای دیدن با استفاده از جهان مجازی
منبع:
Sciencedirect - Elsevier - Image and Vision Computing, Accepted manuscript. doi:10.1016/j.imavis.2017.07.007
نویسنده:
Antonio M. Lopez, Gabriel Villalonga, Laura Sellart, German Ros, David Vazquez, Jiaolong Xu, Javier Mar, Azadeh Mozafari,
چکیده انگلیسی:
Computer vision technologies are at the core of different advanced driver assistance systems (ADAS) and will play a key role in oncoming autonomous vehicles too. One of the main challenges for such technologies is to perceive the driving environment, i.e. to detect and track relevant driving information in a reliable manner (e.g. pedestrians in the vehicle route, free space to drive through). Nowadays it is clear that machine learning techniques are essential for developing such a visual perception for driving. In particular, the standard working pipeline consists of collecting data (i.e. on-board images), manually annotating the data (e.g. drawing bounding boxes around pedestrians), learning a discriminative data representation taking advantage of such annotations (e.g. a deformable part-based model, a deep convolutional neural network), and then assessing the reliability of such representation with the acquired data. In the last two decades most of the research efforts focused on representation learning (first, designing descriptors and learning classifiers; later doing it end-to-end). Hence, collecting data and, especially, annotating it, is essential for learning good representations. While this has been the case from the very beginning, only after the disruptive appearance of deep convolutional neural networks it became a serious issue due to their data hungry nature. In this context, the problem is that manual data annotation is a tiresome work prone to errors. Accordingly, in the late 00’s we initiated a research line consisting in training visual models using photo-realistic computer graphics, especially focusing on assisted and autonomous driving. In this paper, we summarize such a work and show how it has become a new tendency with increasing acceptance.
Keywords: ADAS | Autonomous Driving | Computer Vision | Object Detection | Semantic Segmentation | Machine Learning | Data Annotation | Virtual Worlds | Domain Adaptation
قیمت: رایگان
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