A robust traffic parameter extraction method using texture and entropy

This paper presents a robust traffic parameters extraction (RTPE) method for intelligent traffic system. Firstly a texture-based algorithm is introduced to solve the moving shadow problem, which occurs in traffic lane commonly. Secondly, we propose a robust exponential entropy-based and data-dependent threshold vehicle detection algorithm, named RVD-EXEN algorithm to extract vehicle´s feature from raw visual information for vehicle detection. On this basis, we calculate some basic traffic parameters such as traffic flow, time occupancy ratio and space mean speed. The experiments show that proposed RTPE method has the flexibility to shadow situation, robustness to noise and efficiency of computation.