Real Time Airborne Monitoring for Disaster and Traffic Applications

Remote sensing applications like disaster or mass event monitoring need the acquired data and extracted information within a very short time span. Airborne sensors can acquire the data quickly and on-board processing combined with data downlink is the fastest possibility to achieve this requirement. For this purpose, a new low-cost airborne frame camera system has been developed at the German Aerospace Center (DLR) named 3K-camera. The pixel size and swath width range between 15 cm to 50 cm and 2.5 km to 8 km respectively. Within two minutes an area of approximately 10 km x 8 km can be monitored. Image data are processed onboard on five computers using data from a real time GPS/IMU system including direct georeferencing. Due to high frequency image acquisition (3 images/second) the monitoring of moving objects like vehicles and people is performed allowing wide area detailed traffic monitoring

Vision based real time traffic monitoring

A system and method for detecting and tracking one or more vehicles using a system for obtaining two-dimensional visual data depicting traffic flow on a road is disclosed. In one exemplary embodiment, the system and method identifies groups of features for determining traffic data. The features are classified as stable features or unstable features based on whether each feature is on the frontal face of a vehicle close to the road plane. In another exemplary embodiment, the system and method identifies vehicle base fronts as a basis for determining traffic data. In yet another exemplary embodiment, the system and method includes an automatic calibration procedure based on identifying two vanishing points

Traffic monitoring using image processing : a thesis presented in partial fulfillment of the requirements for the degree of Master of Engineering in Information and Telecommunications Engineering at Massey University, Palmerston North, New Zealand

Traffic monitoring involves the collection of data describing the characteristics of vehicles and their movements. Such data may be used for automatic tolls, congestion and incident detection, law enforcement, and road capacity planning etc. With the recent advances in Computer Vision technology, videos can be analysed automatically and relevant information can be extracted for particular applications. Automatic surveillance using video cameras with image processing technique is becoming a powerful and useful technology for traffic monitoring. In this research project, a video image processing system that has the potential to be developed for real-time application is developed for traffic monitoring including vehicle tracking, counting, and classification. A heuristic approach is applied in developing this system. The system is divided into several parts, and several different functional components have been built and tested using some traffic video sequences. Evaluations are carried out to show that this system is robust and can be developed towards real-time applications

Automatic Estimation of the Exposure to Lateral Collision in Signalized Intersections using Video Sensors

Intersections constitute one of the most dangerous elements in road systems. Traffic signals remain the most common way to control traffic at high-volume intersections and offer many opportunities to apply intelligent transportation systems to make traffic more efficient and safe. This paper describes an automated method to estimate the temporal exposure of road users crossing the conflict zone to lateral collision with road users originating from a different approach. This component is part of a larger system relying on video sensors to provide queue lengths and spatial occupancy that are used for real time traffic control and monitoring. The method is evaluated on data collected during a real world experiment

Real-time Vehicle Detection, Tracking and Counting System Based on YOLOv7

The importance of real-time vehicle detection tracking and counting system based on YOLOv7 is an important tool for monitoring traffic flow on highways. Highway traffic management, planning, and prevention rely heavily on real-time traffic monitoring technologies to avoid frequent traffic snarls, moving violations, and fatal car accidents. These systems rely only on data from timedependent vehicle trajectories used to predict online traffic flow. Three crucial duties include the detection, tracking, and counting of cars on urban roads and highways as well as the calculation of statistical traffic flow statistics (such as determining the real-time vehicles flow and how many different types of vehicles travel). Important phases in these systems include object detection, tracking, categorizing, and counting. The YOLOv7 identification method is presented to address the issues of high missed detection rates of the YOLOv7 algorithm for vehicle detection on urban highways, weak perspective perception of small targets, and insufficient feature extraction. This system aims to provide real-time monitoring of vehicles, enabling insights into traffic patterns and facilitating informed decision-making. In this paper, vehicle detecting, tracking, and counting can be calculated on real-time videos based on modified YOLOv7 with high accuracy