A Strategy to Detect the Moving Vehicle Shadows Based on Gray-Scale Information
Yang Yu, Ming Yu, Yongchao Ma · 2009
In machine vision and the vehicle recognition system, removal of moving vehicle shadows is a significant topic. In this paper, we propose a novel method to detect shadows in traffic video sequences. Firstly, a set of moving regions are segmented from the video sequence using a background subtraction technique. Secondly, the fast normalized cross-correlation (FNCC) is adopted to detect shadows in moving regions from grayscale video sequences. By utilizing three sum-table schemes, the FNCC algorithm dramatically reduces the computational complexity compared to the traditional normalized cross correlation (NCC) algorithm. And our experimental results demonstrate that the proposed shadows removal method is accurate and efficient.