Evaluation of Mobile WiMAX and Intelligent Video for Enhanced Rail Transit Safety
-
2008-06-01
Details:
-
Alternative Title:SharpRAIL: Evaluation of Mobile WiMAX and Intelligent Video for Enhanced Rail Transit Safety
-
Creators:
-
Corporate Creators:
-
Corporate Contributors:
-
Subject/TRT Terms:
-
Publication/ Report Number:
-
Resource Type:
-
Geographical Coverage:
-
Corporate Publisher:
-
Abstract:U.S. rail transit operations have an excellent safety record with fatalities from rail accidents averaging fewer than forty per year. However, the Federal Transit Administration continues to pursue a proactive approach in seeking new technologies that can help in identifying and anticipating safety hazards and preventing accidents. SharpRAIL represents a unique integration of high-speed mobile wireless technologies, advanced computer intelligence, and scalable video processing with versatile command and control to achieve automated detection of incidents and impending collisions. SharpRAIL seeks to exploit advances in 1) artificial intelligence software that can analyze video images in real-time to automatically catch pre-configured security/safety events, such as rail trespass, vehicles trapped on tracks, loitering, criminal activities, etc., and send instant wireless alerts to responsible rail personnel; 2) availability of high-speed mobile wireless technologies in conjunction with industry-wide standardization efforts through mobile/static WiMAX (Worldwide Interoperability for Microwave Access) for high-bandwidth data transport; and 3) sophisticated DSP-based video encoders that can perform intensive image processing right at the "edges”, where the cameras are located. The SharpRAIL project involved design of a prototype solution that achieves these capabilities using commercial, off-the-shelf (COTS) offerings and operational feasibility demonstration through a public-private partnership involving the industry, local government and a public transit agency.
-
Format:
-
Funding:
-
Collection(s):
-
Main Document Checksum:
-
Download URL:
-
File Type: