Deep Learning in the Automotive Industry: Applications and Tools

Deep Learning refers to a set of machine learning techniques that utilize neural networks with many hidden layers for tasks, such as image classification, speech recognition, language understanding. Deep learning has been proven to be very effective in these domains and is pervasively used by many Internet services. In this paper, we describe different automotive uses cases for deep learning in particular in the domain of computer vision. We surveys the current state-of-the-art in libraries, tools and infrastructures (e.\,g.\ GPUs and clouds) for implementing, training and deploying deep neural networks. We particularly focus on convolutional neural networks and computer vision use cases, such as the visual inspection process in manufacturing plants and the analysis of social media data. To train neural networks, curated and labeled datasets are essential. In particular, both the availability and scope of such datasets is typically very limited. A main contribution of this paper is the creation of an automotive dataset, that allows us to learn and automatically recognize different vehicle properties. We describe an end-to-end deep learning application utilizing a mobile app for data collection and process support, and an Amazon-based cloud backend for storage and training. For training we evaluate the use of cloud and on-premises infrastructures (including multiple GPUs) in conjunction with different neural network architectures and frameworks. We assess both the training times as well as the accuracy of the classifier. Finally, we demonstrate the effectiveness of the trained classifier in a real world setting during manufacturing process.Comment: 10 page

RMSRS: Rover Multi-purpose Surveillance Robotic System

أصبح تطوير إنترنت الأشياء (IoT) وإنترنت الروبوتات (IoR) أكثر وأكثر مشاركة في حياتنا اليومية. إنه يخدم مجموعة متنوعة من المهام بعضها مهم في الحياة البشرية مثل المراقبة في الوقت الفعلي عن بعد لتجنب المخاطر في الاماكن الخطرة . الهدف الرئيسي من نظام المراقبة المتنقل الذكي هو تطوير نظام مراقبة للكشف عن الأماكن المشبوهة والمستهدفة للمستخدمين دون أي خسائر في الأرواح البشرية. تعرض هذه الورقة تصميم وتنفيذ منصة مراقبة آلية للمراقبة في الوقت الفعلي بمساعدة معالجة الصور ، والتي يمكن أن تستكشف أماكن الوصول الصعب أو المخاطرة العالية. يتدفق البث المباشر الآلي عبر كاميرتين، الأولى ثابتة مباشرة على الطريق والثانية ديناميكية مع إمكانية الإمالة. كلتا الكامرتين لديها قدرات المعالجة الصورية لتحليل وكشف وتعقب الكائنات بالإضافة إلى عدد قليل من الوظائف الرسومية. المكونات المذكورة أعلاه مبنية على قمة نظام المركبات الرباعي مع عزم دوران عالي لتوفير القدرة على الحركة في المناطق الوعرة. يستند هذا العمل إلى الراسبيري باي ويمكن التحكم فيه عبر الواي فاي محليًا أو عالميا عبر الإنترنت. تظهر النتائج إنشاء روبوت ذو إمكانات عالية ومنخفض الكلفة نسبيًا مع الكثير من الميزات والوظائف التي يمكن أن تؤدي مهام متعددة في وقت واحد ، وكلها مهمة للغاية بالنسبة لمشاكل المراقبة ، والتي يتحكم فيها المستخدم من مسافات بعيدة ولفترة طويلة.The development of the internet of things (IoT) and the internet of robotics (IoR) are becoming more and more involved with our daily lives. It serves a variety of tasks some of them are essential to us. The main objective of SRR is to develop a surveillance system for detecting suspicious and targeted places for users without any loss of human life. This paper shows the design and implementation of a robotic surveillance platform for real-time monitoring with the help of image processing, which can explorer places of difficult access or high risk. The robotic live streaming is via two cameras, the first one is fixed straight on the road and the second one is dynamic with tilt-pan ability. All cameras have image processing capabilities to analyze, detect and track objects plus few other graphical functions. The components mentioned above built on top of the four-wheel vehicle system with high torque to provide mobility on rough terrain. This work is based on Raspberry Pi and can be controlled over Wi-Fi locally or publicly over the internet. The results show making a high potential, relatively low price robot with lots of features and functions that can perform multiple tasks simultaneously, all are crucial to surveillance and monitoring problems, controlled by a user from far distances and for a long time

Edge Video Analytics: A Survey on Applications, Systems and Enabling Techniques

Video, as a key driver in the global explosion of digital information, can create tremendous benefits for human society. Governments and enterprises are deploying innumerable cameras for a variety of applications, e.g., law enforcement, emergency management, traffic control, and security surveillance, all facilitated by video analytics (VA). This trend is spurred by the rapid advancement of deep learning (DL), which enables more precise models for object classification, detection, and tracking. Meanwhile, with the proliferation of Internet-connected devices, massive amounts of data are generated daily, overwhelming the cloud. Edge computing, an emerging paradigm that moves workloads and services from the network core to the network edge, has been widely recognized as a promising solution. The resulting new intersection, edge video analytics (EVA), begins to attract widespread attention. Nevertheless, only a few loosely-related surveys exist on this topic. The basic concepts of EVA (e.g., definition, architectures) were not fully elucidated due to the rapid development of this domain. To fill these gaps, we provide a comprehensive survey of the recent efforts on EVA. In this paper, we first review the fundamentals of edge computing, followed by an overview of VA. The EVA system and its enabling techniques are discussed next. In addition, we introduce prevalent frameworks and datasets to aid future researchers in the development of EVA systems. Finally, we discuss existing challenges and foresee future research directions. We believe this survey will help readers comprehend the relationship between VA and edge computing, and spark new ideas on EVA.Comment: 31 pages, 13 figure

Indexing, browsing and searching of digital video

Video is a communications medium that normally brings together moving pictures with a synchronised audio track into a discrete piece or pieces of information. The size of a “piece ” of video can variously be referred to as a frame, a shot, a scene, a clip, a programme or an episode, and these are distinguished by their lengths and by their composition. We shall return to the definition of each of these in section 4 this chapter. In modern society, video is ver

TechNews digests: Jan - Mar 2010

TechNews is a technology, news and analysis service aimed at anyone in the education sector keen to stay informed about technology developments, trends and issues. TechNews focuses on emerging technologies and other technology news. TechNews service : digests september 2004 till May 2010 Analysis pieces and News combined publish every 2 to 3 month

VANET Applications: Hot Use Cases

Current challenges of car manufacturers are to make roads safe, to achieve free flowing traffic with few congestions, and to reduce pollution by an effective fuel use. To reach these goals, many improvements are performed in-car, but more and more approaches rely on connected cars with communication capabilities between cars, with an infrastructure, or with IoT devices. Monitoring and coordinating vehicles allow then to compute intelligent ways of transportation. Connected cars have introduced a new way of thinking cars - not only as a mean for a driver to go from A to B, but as smart cars - a user extension like the smartphone today. In this report, we introduce concepts and specific vocabulary in order to classify current innovations or ideas on the emerging topic of smart car. We present a graphical categorization showing this evolution in function of the societal evolution. Different perspectives are adopted: a vehicle-centric view, a vehicle-network view, and a user-centric view; described by simple and complex use-cases and illustrated by a list of emerging and current projects from the academic and industrial worlds. We identified an empty space in innovation between the user and his car: paradoxically even if they are both in interaction, they are separated through different application uses. Future challenge is to interlace social concerns of the user within an intelligent and efficient driving