Abstract
Road traffic density has always been a concern in large cities around the world, and many approaches were developed to assist in solving congestions related to slow traffic flow. This work proposes a congestion rate estimation approach that relies on real-time video scenes of road traffic, and was implemented and evaluated on eight different hotspots covering 33 different urban roads. The approach relies on road scene morphology for estimation of vehicles average speed along with measuring the overall video scenes randomness acting as a frame texture analysis indicator. Experimental results shows the feasibility of the proposed approach in reliably estimating traffic density and in providing an early warning to drivers on road conditions, thereby mitigating the negative effect of slow traffic flow on their daily lives.
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Al-Kadi, O., Al-Kadi, O., Al-Sayyed, R. et al. Road scene analysis for determination of road traffic density. Front. Comput. Sci. 8, 619–628 (2014). https://doi.org/10.1007/s11704-014-3156-0
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DOI: https://doi.org/10.1007/s11704-014-3156-0