Real-Time Machine Learning Models for Intelligent Transportation Infrastructure (ITI) and Surveillance Systems for Urban Environments (Volume II)

Dear Colleagues,

This Special Issue is a continuation of our previous Special Issue, “Machine Learning Methods for Intelligent Transportation Infrastructure (ITI) Systems for Urban Environments I”.

The volume of motor traffic is increasing day by day on our roadways, and it is essential to improve traffic management systems by ensuring road safety and mobility in a reliable way. Traffic noise exposure, air pollution, road injuries, and traffic delays are some of the major problems experienced daily by residents of urban areas. Urban cities are faced with serious environmental and quality-of-life challenges, which have arisen due to the significant increase in vehicles, inadequate transport infrastructure, and lack of road-safety policies. For example, in many urban cities, traffic congestion and delays occur due to heavy trucks driving on normal roadways. In addition, cyclists often experience near-misses because the recognition rates of machine learning (ML) algorithms are affected by cyclists’ clothing and posture changes, partial occlusions, and different observation angles.

The last ten years have seen increasing interest in the use of machine learning and deep learning methods to improve the classification and recognition of pedestrians, bicycles, and special vehicles (e.g., emergency vehicles vs. heavy trucks), as well as license plate recognition (LPR), for a safer and more sustainable environment. Although deep models can capture a wide variety of objects, environmental adaptation is required.

The aim of this Special Issue is to highlight modern intelligent transportation infrastructure systems by publishing original, innovative, and state-of-the-art machine learning methods, algorithms, and architectures. Innovative solutions, in the form of efficient visual object-learning algorithms, prediction models, and environmental sensors, are invited. Topics of interest include but are not limited to:

• Surveillance systems and solutions;
• Automatic license plate recognition (ALPR) methods;
• Multi-camera systems;
• Information fusion (e.g., from visible and infrared cameras, microphone arrays);
• Learning systems, cognitive system engineering and video mining;
• Real-time computer vision algorithms (24/7 operation under variable conditions, object tracking, multi-camera algorithms, behavior analysis and learning, scene segmentation);
• Human–machine interfaces, human system engineering, and human factors;
• Algorithmic bias and transparency for machine learning.

Dr. Anastassia Angelopoulou
Dr. Jude Hemanth 
Dr. Peter M. Roth
Dr. Epameinondas Kapetanios
Prof. Dr. Jose Garcia Rodriguez 
Guest Editors

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