Moving vehicle detection for automatic traffic monitoring

2006-2007 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

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

Map++: A Crowd-sensing System for Automatic Map Semantics Identification

Digital maps have become a part of our daily life with a number of commercial and free map services. These services have still a huge potential for enhancement with rich semantic information to support a large class of mapping applications. In this paper, we present Map++, a system that leverages standard cell-phone sensors in a crowdsensing approach to automatically enrich digital maps with different road semantics like tunnels, bumps, bridges, footbridges, crosswalks, road capacity, among others. Our analysis shows that cell-phones sensors with humans in vehicles or walking get affected by the different road features, which can be mined to extend the features of both free and commercial mapping services. We present the design and implementation of Map++ and evaluate it in a large city. Our evaluation shows that we can detect the different semantics accurately with at most 3% false positive rate and 6% false negative rate for both vehicle and pedestrian-based features. Moreover, we show that Map++ has a small energy footprint on the cell-phones, highlighting its promise as a ubiquitous digital maps enriching service.Comment: Published in the Eleventh Annual IEEE International Conference on Sensing, Communication, and Networking (IEEE SECON 2014

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