–with an average accuracy of 94.63 % for sign classification– show the potential of the method to provide quick, inexpensive, and automatic access to asset inventory information. The experiments conducted on 6.2 miles of I-57 and I-74 interstate highways in the U.S. Finally, several data mining interfaces are introduced that allow for better management of the traffic sign inventories. A data card containing information about location and type of each detected traffic sign is also created.
Considering the discriminative classification scores from all images that see a sign, the most probable location of each traffic sign is derived and shown on the Google Maps using a dynamic heat map. Using computer vision method, traffic signs are detected and classified into four categories of regulatory, warning, stop, and yield signs by processing images extracted from Google Street View API. The key benefit of such databases is that once traffic signs are detected, their geographic coordinates can also be derived and visualized within the same platform.īy leveraging Google Street View images, this paper presents a new system for creating inventories of traffic signs. While, databases such as Google Street View contain street-level images of all traffic signs and are updated regularly, their potential for creating an inventory databases has not been. Maintaining an up-to-date record of the number, type, location, and condition of high-quantity low-cost roadway assets such as traffic signs is critical to transportation inventory management systems. The managed network is divided into several domains and for each Read more Second, we propose a distributed framework for intelligent fault management purpose. We propose three system designs based on such ideas. Third, the callback mechanism through whichthe functionality of the delegated agents or even the native software could be extended. Second, the collection API for our delegated agents to collect data from network elements. First, the design of the standard infrastructure, or Virtual Machine, based on which agents could be created, deployed, managed and initiated to run. The focus of our work is on three aspects.
implement a distributed, extensible framework for supporting adaptive, dynamic network monitoring and control. First, we choose the mobile code technology to help. The dissertation consists of four major parts. The architecture is based on a distributed agent paradigm, with belief networks as the framework for knowledge representation and evidence propagation. This dissertation is devoted to the design of an intelligent, distributed fault and performance management system for communication networks.
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