As technology continues to develop, computer vision (CV) applications are becoming increasingly widespread in the intelligent transportation systems (ITS) context. These applications are developed to improve the efficiency of transportation systems, increase their level of intelligence, and enhance traffic safety. Advances in CV play an important role in solving problems in the fields of traffic monitoring and control, incident detection and management, road usage pricing, and road condition monitoring, among many others, by providing more effective methods. This survey examines CV applications in the literature, the machine learning and deep learning methods used in ITS applications, the applicability of computer vision applications in ITS contexts, the advantages these technologies offer and the difficulties they present, and future research areas and trends, with the goal of increasing the effectiveness, efficiency, and safety level of ITS. The present review, which brings together research from various sources, aims to show how computer vision techniques can help transportation systems to become smarter by presenting a holistic picture of the literature on different CV applications in the ITS context.

Transformer is a kind of deep neural network that relies on the technique of self-attention and used initially in the field of natural language processing. Scientists use transformer for computer vision (CV) applications because of its good data representation capabilities. Transformer-based models yield similar performance or surpass other network architectures, including convolutional and recurrent neural networks, in a variety of visual benchmarks. In this work, we investigate the methods for video anomaly detection (VAD) using vision transformer models in the recent literature. The main topics we explore comprise vision transformers used in CV applications with a special focus on VAD methods leveraging transformer architecture. We also briefly present anomaly detection methods based on transformers. Additionally, we address the advantages, challenges and current limitations of the transformer architecture as well as potential solutions to address the technical challenges. In the concluding section of this study, we offer avenues for further investigation concerning the use of vision transformers in VAD tasks.

The use of surveillance cameras is a common solution for the need to manage the urban traffic that arises with the effect of the increment in the population in the cities and to provide security. As the number of surveillance cameras rises, video streams that create big data are recorded. Analysis of video streams collected from those traffic surveillance cameras, automatic detection of unusual, suspicious events, as well as a range of harmful activities, become crucial because it is impossible to observe, analyze, and comprehend the contents of these movies using human labor. Recent studies have shown that deep learning (DL)-based artificial intelligence (AI) techniques, particularly machine learning (ML) techniques, are used in video anomaly detection (VAD) studies. In this study, an efficient frame-level VAD method is proposed based on transfer learning (TL) and fine-tuning (FT) approach and anomalies were detected using 20 popular convolutional neural networks (CNN) based DL models where variants of VGG, Xception, MobileNet, Inception, EfficientNet, ResNet, DenseNet, NASNet and ConvNeXt base models were trained using TL and FT approach. The proposed approach was tested using CUHK Avenue, UCSD Ped1 and UCSD Ped2 datasets and the performance of the models were measured using Area Under Curve (AUC), accuracy, precision, recall, and F1-score metrics. The highest AUC scores measured were 100%, 100% and 98.41% for the UCSD Ped1, UCSD Ped2 and CUHK Avenue datasets respectively. Compared to existing techniques in the literature, experimental results show that the suggested method offers state-of-the-art (SOTA) VAD performance.

Akıllı Ulaşım Sistemleri (AUS) trafik sıkışıklığı, emniyet ve hava kirliliği gibi ulaşım altyapısının karşı karşıya olduğu birçok önemli sorunu çözmek için dünyada yaygın olarak kullanılmaktadır. AUS mimarisi, AUS uygulamalarını planlamak, tanımlamak, entegre etmek, birlikte çalışabilirliği sağlamak, ulaşım faaliyetlerini iyileştirerek verimliliği artırmak ve yeni kullanıcı hizmetlerini desteklemek için AUS uygulamalarının geliştirilmesine yönelik bir çerçeve sunmaktadır. Bu çalışmada, AUS mimarilerinin dünyadaki tarihsel gelişimiyle birlikte öne çıkan ARC-IT, FRAME, ISO referans AUS mimarileri literatür doğrultusunda incelenerek AUS mimarisinin önemine, faydalarına, bileşenlerine, AUS mimarisi geliştirmek için kullanılan araçlara değinilmiştir. Dünyadaki ulusal ve bölgesel AUS mimarilerinin geliştirilmesine yönelik çalışmalar özetlendikten sonra Türkiye’deki mevcut durum ele alınmış ve Ulusal AUS Mimarisinin kullanımının yaygınlaştırılması amacıyla Türkiye için bir model önerilmiştir.

Siber saldırılar, dijitalleşmiş ve bilgi toplumuna dönüşen ülkelerde, ulusal güvenlik açısından dikkate alınması gereken önemli hususlar arasındadır. Geleneksel fiziksel saldırılardan farklı olarak siber saldırıların ne şekilde meydana geleceği, hangi saldırı yüzeylerini kullanacağı ve hangi hedeflere yöneleceği konuları beklenmedik şekillerde ve çeşitliliktedir. Siber dünyada meydana gelen savaşların sonuçları farklı boyutlarda öngörülemeyen etkilere sahip olabilmektedir. Bu durumun dünyada ses getiren örneklerinden biri, 2007 yılında Estonya’ya yönelik olarak meydana gelmiştir. Avrupa’nın en teknolojik ülkelerinden olan Estonya, o tarihe kadar tek bir ülkeye yönelik düzenlenen, koordineli, en kapsamlı siber saldırılardan birine maruz kalmıştır. Gelişmiş siber savunma yeteneklerine sahip olmanın önemini vurgulayan, Estonya’ya yönelik bu siber savaş dalgası, ulusal güvenliğin sağlanması için siber güvenlik alanında odaklanılması gereken konuları gün yüzüne çıkarmıştır. Bu çalışmada, 2007 yılında, Estonya’ya düzenlenen siber saldırılar, bu saldırıların nedenleri, hedefleri, ulusal ve uluslararası etkileri, alınan siber savunma önlemleri, saldırılar sonrasında öğrenilmiş dersler incelenmiştir. Bu dersler ışığında, Uluslararası Telekomünasyon Birliği (ITU) Küresel Siber Güvenlik İndeksinde üst sıralarda yer alan ülkelerin ve Türkiye’nin güncel siber güvenlik politikaları değerlendirilmiştir.

Information and communication technologies, facilitate access to information, record a continuous change and development and become an inalienable part of our lives by being very widespread, offer us many opportunities, in the meantime bring cyber security threats attendantly. Cyber espionage, which is the activity of nonconsensual infiltrating of devices with information technology infrastructure in order to collect or destroy information using many different techniques, is one of the cyber security threats that affect the trade, industry and long-term economic development of the exposed countries. In this study, the characteristics of cyber espionage, the tools and methods used in cyber espionage activities, the stages of it, the current situation in our country and in the world are examined, and the cyber espionage events reflected in the literature, the detection of cyber espionage, statistical information on cyber espionage activities, the measures that can be taken to overcome cyber espionage and the useful resources are presented.