An improved shadow removal algorithm based on gradient amendment

This paper presents an improved method for removing cast shadows from multiple objects in a static background using gradient amendment. Shadow can decrease object detection rate and increase the likelihood of tracking failure, which are the two most important measure of algorithm in intelligent video system. The drawback of existed shadow detection methods is the fracture problem of objects, especially for pixel-based method such as chromaticity-based method and texture-based method. A novel method based on gradient amendment is proposed to reduce the possibility of this issue, which has obvious advantages on the protection of object's integrity and the resistance of objects' shadow. We have verified the improved method on a series of indoor and outdoor video sequences.

[1]  Mohan M. Trivedi,et al.  Detecting Moving Shadows: Algorithms and Evaluation , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Zoran Zivkovic,et al.  Improved adaptive Gaussian mixture model for background subtraction , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[3]  Larry S. Davis,et al.  W4: Real-Time Surveillance of People and Their Activities , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Brian C. Lovell,et al.  Improved Shadow Removal for Robust Person Tracking in Surveillance Scenarios , 2010, 2010 20th International Conference on Pattern Recognition.

[5]  Nicolas Martel-Brisson,et al.  Kernel-based learning of cast shadows from a physical model of light sources and surfaces for low-level segmentation , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Richard Bowden,et al.  A real time adaptive visual surveillance system for tracking low-resolution colour targets in dynamically changing scenes , 2003, Image Vis. Comput..

[7]  Z. Zivkovic Improved adaptive Gaussian mixture model for background subtraction , 2004, ICPR 2004.

[8]  Larry S. Davis,et al.  Real-time foreground-background segmentation using codebook model , 2005, Real Time Imaging.

[9]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[10]  Larry S. Davis,et al.  A Robust Background Subtraction and Shadow Detection , 1999 .

[11]  Qi Zhang,et al.  Medical Image Retrieval Using Local Binary Patterns with Image Euclidean Distance , 2009, 2009 International Conference on Information Engineering and Computer Science.

[12]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Mohan M. Trivedi,et al.  Moving shadow and object detection in traffic scenes , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[14]  Wen-Chung Kao,et al.  An enhanced segmentation on vision-based shadow removal for vehicle detection , 2010, The 2010 International Conference on Green Circuits and Systems.

[15]  Alessandro Leone,et al.  Shadow detection for moving objects based on texture analysis , 2007, Pattern Recognit..

[16]  Rita Cucchiara,et al.  Detecting Moving Objects, Ghosts, and Shadows in Video Streams , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Brian C. Lovell,et al.  Shadow detection: A survey and comparative evaluation of recent methods , 2012, Pattern Recognit..

[18]  A. Suruliandi,et al.  Local binary pattern and its derivatives for face recognition , 2012 .

[19]  Richard Bowden,et al.  Adaptive Visual System for Tracking Low Resolution Colour Targets , 2001, BMVC.

[20]  Bin Li,et al.  Palmprint Identification using Boosting Local Binary Pattern , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[21]  Rita Cucchiara,et al.  The Sakbot System for Moving Object Detection and Tracking , 2002 .

[22]  Marc Van Droogenbroeck,et al.  ViBe: A Universal Background Subtraction Algorithm for Video Sequences , 2011, IEEE Transactions on Image Processing.