Real-Time Road Traffic Density Estimation Using Block Variance

Abstract

The increasing demand for urban mobility calls for a robust real-time traffic monitoring system. In this paper we present a vision-based approach for road traffic density estimation which forms the fundamental building block of traffic monitoring systems. Existing techniques based on vehicle counting and tracking suffer from low accuracy due to sensitivity to illumination changes, occlusions, congestions etc. In addition, existing holistic-based methods cannot be implemented in real-time due to high computational complexity. In this paper we propose a block based holistic approach to estimate traffic density which does not rely on pixel based analysis, therefore significantly reducing the computational cost. The proposed method employs variance as a means for detecting the occupancy of vehicles on pre-defined blocks and incorporates a shadow elimination scheme to prevent false positives. In order to take into account varying illumination conditions, a low-complexity scheme for continuous background update is employed. Empirical evaluations on publicly available datasets demonstrate that the proposed method can achieve real-time performance and has comparable accuracy with existing high complexity holistic methods.