An Efficient and Robust Moving Shadow Removal Algorithm and Its Applications in ITS

Abstract

We propose an efficient algorithm for removing shadows of moving vehicles caused by non-uniform distributions of light reflections in the daytime. This paper presents a brand-new and complete structure in feature combination as well as analysis for orientating and labeling moving shadows so as to extract the defined objects in foregrounds more easily in each snapshot of the original files of videos which are acquired in the real traffic situations. Moreover, we make use of Gaussian Mixture Model (GMM) for background removal and detection of moving shadows in our tested images, and define two indices for characterizing non-shadowed regions where one indicates the characteristics of lines and the other index can be characterized by the information in gray scales of images which helps us to build a newly defined set of darkening ratios (modified darkening factors) based on Gaussian models. To prove the effectiveness of our moving shadow algorithm, we carry it out with a practical application of traffic flow detection in ITS (Intelligent Transportation System)-vehicle counting. Our algorithm shows the faster processing speed, 13.84 ms/frame, and can improve the accuracy rate in 4% ∼10% for our three tested videos in the experimental results of vehicle counting.

Shadow Detection Using Color and Edge Information

“…These methods rely on geometric information for the scene and objects and the illumination of the scene, such as the sensor or camera location, the light source direction, the ground surface and the object geometry ( Kumar and Kaur, 2010). The model based methods which are based on geometric information can detect shadows effectively in limited and simulated environments because of the geometric relations between objects and scenes ( Lin et al, 2010;Zhou and Xiaobo, 2010). Besides, all the geometrical models strongly depend on the geometrical relationship between the objects in the scenes and when the geometrical relationships change, these methods can no longer be effective ( Sun and Li, 2010;Zhang and Wu, 2010).…”

“…Besides, all the geometrical models strongly depend on the geometrical relationship between the objects in the scenes and when the geometrical relationships change, these methods can no longer be effective ( Sun and Li, 2010;Zhang and Wu, 2010). In addition, this is not suitable for spatial real-time cases due to the heavy computational load ( Lin et al, 2010). As an example of this kind of methods, foreground segmentation is done by noise level adapted method in Wei-Gang and Bin (2010).…”

“…This model is based on the fact that the texture of objects is different from the texture of the background, while the texture of a shadowed region remains the same as the background and this is distributed uniformly ( Kumar and Kaur, 2010;Lin et al, 2010;Zhang and Wu, 2010). The texture model gives more accurate results under stable illumination conditions such as indoor environments without the color information.…”

“…Moreover, the texture model gives the poorest results for textureless objects. In addition, the texture method does not have a significant performance when the assumption of the algorithm concerning the shadow is not preserved (Lin et al, 2010;Vargas et al, 2010). Furthermore, this technique is difficult in implementation ( Zhang and Wu, 2010).…”

Shadow Detection Approach Combining Spectral and Geometrical Properties in Highway Video-Surveillance

“…Finally, morphological operators are applied, to remove isolated foreground pixels. [15] proposed an algorithm for removing shadows by combining texture and statistical models. They use a Gaussian Mixture Model for background removal and the detection of moving shadows in test images.…”