An efficient and robust moving shadow removal algorithm and its applications in ITS

Authors:
Chin-Teng Lin;Chien-Ting Yang;Yu-Wen Shou;Tzu-Kuei Shen
Affiliations:
Department of Electrical and Control Engineering, National Chiao Tung University, Hsinchu, Taiwan;Department of Electrical and Control Engineering, National Chiao Tung University, Hsinchu, Taiwan;Department of Computer and Communication Engineering, China University of Technology, Hsinchu, Taiwan;Department of Electrical and Control Engineering, National Chiao Tung University, Hsinchu, Taiwan
Venue:
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
Year:
2010

Citing 9
Cited 1

Image difference threshold strategies and shadow detection

BMVC '95 Proceedings of the 1995 British conference on Machine vision (Vol. 1)
Moving Shadow and Object Detection in Traffic Scenes

ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
A Dynamic Conditional Random Field Model for Foreground and Shadow Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning and Removing Cast Shadows through a Multidistribution Approach

IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to Detect Moving Shadows in Dynamic Environments

IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic traffic surveillance system for vehicle tracking and classification

IEEE Transactions on Intelligent Transportation Systems
Moving Cast Shadows Detection Using Ratio Edge

IEEE Transactions on Multimedia
Detecting moving objects, ghosts, and shadows in video streams

IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Foreground and Shadow Detection in Uncertain Frame Rate Surveillance Videos

IEEE Transactions on Image Processing

A survey of cast shadow detection algorithms

Pattern Recognition Letters

Quantified Score

Hi-index	0.00

Visualization

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.