Novel Approach for Detection and Removal of Moving Cast Shadows Based on RGB, HSV and YUV Color Spaces

Authors

  • Brahim Farou Labstic, Guelma University
  • Houssam Rouabhia Labstic, Guelma University
  • Hamid Seridi Labstic, Guelma University
  • Herman Akdag LIASD, Paris 8 University, Saint-Denis

Keywords:

Computer vision, shadow detection, chromaticity, GMM

Abstract

Cast shadow affects computer vision tasks such as image segmentation, object detection and tracking since objects and shadows share the same visual motion characteristics. This unavoidable problem decreases video surveillance system performance. The basic idea of this paper is to exploit the evidence that shadows darken the surface which they are cast upon. For this reason, we propose a simple and accurate method for detection of moving cast shadows based on chromatic properties in RGB, HSV and YUV color spaces. The method requires no a priori assumptions regarding the scene or lighting source. Starting from a normalization step, we apply canny filter to detect the boundary between self-shadow and cast shadow. This treatment is devoted only for the first sequence. Then, we separate between background and moving objects using an improved version of Gaussian mixture model. In order to remove these unwanted shadows completely, we use three change estimators calculated according to the intensity ratio in HSV color space, chromaticity properties in RGB color space, and brightness ratio in YUV color space. Only pixels that satisfy threshold of the three estimators are labeled as shadow and will be removed. Experiments carried out on various video databases prove that the proposed system is robust and efficient and can precisely remove shadows for a wide class of environment and without any assumptions. Experimental results also show that our approach outperforms existing methods and can run in real-time systems.

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Author Biographies

Brahim Farou, Labstic, Guelma University

Brahim Farou received his magister degree in computer science from Guelma University. He is an Assistant Professor in Computer science department and member in LabSTIC laboratory at the University of Guelma. His research interests include, video mining, human behavior, the extraction of moving objects, shadow detection and removal, image segmentation.

Houssam Rouabhia, Labstic, Guelma University

Houssam Rouabhia is Phd student in computer science department and member in LabSTIC laboratory at the University of Guelma. His research interests include pattern recognition, image segmentation and shadow detection.

Hamid Seridi, Labstic, Guelma University

Hamid Seridi received his Master’s degree from the Polytechnic Institute of New-York, USA in 1984, both in Electrical Engineering. He received his PhD in Computer Science with distinction from the University of Reims, France. He was responsible of the National Graduate School of Science and Information Technologies and Communication. From August 2005 through December 2010, he was Vice Dean of the Post Graduation, Scientific Research and External Relations in the University of Guelma. Currently he is Professor and Director of Laboratory of Science and Information Technologies and Communication ‘‘LabSTIC’’ in the University of Guelma. He is an expert member at the national committee for evaluation and accreditation national projects research. His research interests include approximate knowledge management, pattern recognition and artificial intelligence, data mining, video mining, machine learning and cryptography.

Herman Akdag, LIASD, Paris 8 University, Saint-Denis

Herman Akdag received his PhD and HDR degree at Paris VI University in 1980 and 1992, respectively. Assistant Professor since 1980, he obtained a Full Professor position at Reims University (France) in 1995. Actually, he is a Senior Researcher at LIASD laboratory university of Paris 8, France. His research interests include Fuzzy Set Theory and Machine Learning approaches to decision-making, image classification and image retrieval. He also works on approximate reasoning, fuzzy abduction, data mining, and user modelling.

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Published

2017-11-29

How to Cite

Farou, B., Rouabhia, H., Seridi, H., & Akdag, H. (2017). Novel Approach for Detection and Removal of Moving Cast Shadows Based on RGB, HSV and YUV Color Spaces. COMPUTING AND INFORMATICS, 36(4), 837–856. Retrieved from http://www.cai.sk/ojs/index.php/cai/article/view/2017_4_837