Sensor networks and associated infrastructures become ever more important to the traffic monitoring and control because of the increasing traffic demands in terms of congestion and safety. These systems allow authorities not only to monitor the traffic state at the detection sites, but also to obtain real-time related information (e.g. traffic loads). This study presents a real-time vision system for automatic traffic monitoring based on a network of autonomous tracking units (ATUs) that capture and process images from one or more pre-calibrated cameras. The proposed system is flexible, scalable and suitable for a broad field of applications, including traffic monitoring of tunnels at highways and aircraft parking areas at airports. Another objective of this work is to test and evaluate different image processing and data fusion techniques in order to be incorporated to the final system. The output of the image processing unit is a set of information for each moving object in the scene, such as target ID, position, velocity and classification, which are transmitted to a remote traffic control centre, with remarkably low bandwidth requirements. This information is analysed and used to provide real-time output (e.g. alerts, electronic road signs, ramp meters etc.) as well as to extract useful statistical information (traffic loads, lane changes, average velocity etc.).

In this paper, a detailed study on crime classification and prediction using deep learning architectures is presented. We examine the effectiveness of deep learning algorithms in this domain and provide recommendations for designing and training deep learning systems for predicting crime areas, using open data from police reports. Having time-series of crime types per location as training data, a comparative study of 10 state-of-the-art methods against 3 different deep learning configurations is conducted. In our experiments with 5 publicly available datasets, we demonstrate that the deep learning-based methods consistently outperform the existing best-performing methods. Moreover, we evaluate the effectiveness of different parameters in the deep learning architectures and give insights for configuring them to achieve improved performance in crime classification and finally crime prediction.

The paper presents a real-time vision system for automatic traffic monitoring based on a network of autonomous tracking units that capture and process images from one or more pre-calibrated cameras. The system, which has been developed within the framework of TRAVIS (TRAffic VISual monitoring) project, is flexible, scalable and can be useable for a broad field of applications, including traffic monitoring of tunnels at highways and aircraft parking areas at airports. Different image processing and data fusion techniques were tested and evaluated in order to be incorporated to the system. The final output of the image processing is a set of information for each moving object in the scene, such as target ID, position, velocity and classification. This can be transmitted to a remote traffic control centre, as the network demands are remarkably low. This information is analyzed and used to provide real-time output (e.g. alerts, electronic road signs, ramp meters, etc) as well as to extract useful statistical information (traffic loads, lane changes, average velocity etc).

In this paper a comparative study of three ground traffic surveillance systems for airports based on different sensing technologies is presented. The first two solutions are based on video-based surveillance, while the third one is based on signal-based surveillance. More specifically, the first solution employs a network of smart cameras, whereas the second utilizes multiple autonomous tracking units to capture and process images from one or more pre-calibrated cameras. The third solution is based on a novel magnetic sensing technology, which detects ferromagnetic objects such as vehicle motors, aircraft engines and landing gears through their deformation of the Earthpsilas magnetic field. Evaluation and analysis of the performance of these systems, based on real test results in different European airports are presented. The proposed systems are flexible, scalable and suitable for a broad field of monitoring and surveillance applications.