Implementation For example, the parameter T of GMM is described as follows: ? Name: T ? Value: P min = 0.2, P max = 0.8, ? = 0.05. ? Error indicator: I noise ? Preference: Low With this interface, the two tuning algorithms can work with various background subtraction algorithms. We have tested these tuning algorithms with EGMM and GMM. For the contex-based parameter tuning method to tune the parameters of the background subtraction algorithm EGMM to detect strong shadow in outdoor scenes, we are developing and have not nished it yet. We will nish this functionality in the near future, Bibliography [ATON ] ATON. Project ATON, vol.170, pp.161-169 ,
Design and Assessment of an Intelligent Activity Monitoring Platform, EURASIP Journal on Advances in Signal Processing, vol.2005, issue.14, pp.23592374-217, 2005. ,
DOI : 10.1155/ASP.2005.2359
URL : https://hal.archives-ouvertes.fr/hal-00784483
Classication of digital camera-models based on demosaicing artifacts, Digital Investigation, pp.4959-61, 2008. ,
Study on color space selection for detecting cast shadows in video surveillance, International Journal of Imaging Systems and Technology, vol.28, issue.3, pp.479482-479494, 2007. ,
DOI : 10.1002/ima.20110
Eective Shadow Detection in Trac Monitoring Applications. The 11-th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, pp.189196-103, 2003. ,
A Novel Shadow Detection Algorithm for Real Time Visual Surveillance Applications. Lecture notes in computer science - Image Analysis and Recognition, pp.906917-103, 2006. ,
Illumination for computer generated pictures, Communications of the ACM, vol.18, issue.54, pp.311317-311361, 1975. ,
Detecting shadows in image sequences, Proceedings of the 1st European Conference on Visual Media Production, pp.165174-165186, 2004. ,
Online evaluation of tracking algorithm performance, The 3rd International Conference on Imaging for Crime Detection and Prevention, p.118, 2009. ,
URL : https://hal.archives-ouvertes.fr/inria-00486479
Indoor and outdoor people detection and shadow suppression by exploiting HSV color information, Proceedings of the The Fourth International Conference on Computer and Information Technology, pp.132142-132175, 2004. ,
DOI : 10.1007/s11460-008-0083-6
Flexible background mixture models for foreground segmentation [color shadow ] color shadow. Colored shadow, Journal of Image and Vision Computing, vol.24, issue.56, pp.473482-473501, 2006. ,
Detecting moving objects, ghosts, and shadows in video streams, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.25, issue.10, pp.13371342-13371377, 2003. ,
DOI : 10.1109/TPAMI.2003.1233909
Background estimation and adaptation model with light-change removal for heavily downsampled video surveillance signals, Proceedings of the IEEE International Conference on Image Processing, pp.18291832-18291855, 2006. ,
Recovering high dynamic range radiance maps from photographs, pp.369378-59, 1997. ,
"Hybrid Cone-Cylinder" Codebook Model for Foreground Detection with Shadow and Highlight Suppression, 2006 IEEE International Conference on Video and Signal Based Surveillance, p.25, 2006. ,
DOI : 10.1109/AVSS.2006.1
Non-parametric Model for Background Subtraction, Proceedings of European Conference on Computer Vision, pp.751767-70, 2000. ,
DOI : 10.1007/3-540-45053-X_48
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.592.3233
Internal Technical Note, Data structure and output format ,
Layered detection for multiple overlapping objects, Object recognition supported by user interaction for service robots, pp.156161-136, 2002. ,
DOI : 10.1109/ICPR.2002.1047422
Program Supervision Techniques for Easy Conguration of Video Understanding Systems, p.144, 2006. ,
Real-time control of video surveillance systems with program supervision techniques, Machine Vision and Applications, vol.36, issue.3, pp.189205-143, 2007. ,
DOI : 10.1007/s00138-006-0053-z
URL : https://hal.archives-ouvertes.fr/inria-00502779
Modeling the Space of Camera Response Functions, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.58, pp.12721282-12721326, 2004. ,
A background model initialization algorithm for video surveillance, International Conference on Computer Vision, ICCV 2001, pp.733740-733753, 2001. ,
Adaptive video background modeling using color and depth, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205), p.44, 2001. ,
DOI : 10.1109/ICIP.2001.958058
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.124.2059
A texture-based method for modeling the background and detecting moving objects, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, issue.4, pp.657662-657696, 2006. ,
DOI : 10.1109/TPAMI.2006.68
Shadow elimination for eective moving object detection by Gaussian shadow modeling, Image and Vision Computing, vol.21, pp.505516-505547, 2003. ,
Learning Moving Cast Shadows for Foreground Detection. The Eighth International Workshop on Visual Surveillance, p.33, 2008. ,
URL : https://hal.archives-ouvertes.fr/inria-00325645
A physical approach to Moving Cast Shadow Detection, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.769772-769805, 2009. ,
DOI : 10.1109/ICASSP.2009.4959697
Background Subtraction and Shadow Detection in Grayscale Video Sequences, XVIII Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI'05), pp.189196-103, 2005. ,
DOI : 10.1109/SIBGRAPI.2005.15
A real-time adaptive visual surveillance system for tracking low resolution color targets in dynamically changing scenes, Journal of Image and Vision Computing, vol.21, pp.913929-913947, 2003. ,
Background modeling and subtraction by codebook construction, Proceedings of the International Conference on Image Processing, pp.30613064-203, 2004. ,
Real-time foreground???background segmentation using codebook model, Real-Time Imaging, vol.11, issue.3, pp.29-42, 2005. ,
DOI : 10.1016/j.rti.2004.12.004
URL : https://zenodo.org/record/9800
The Art of Computer Programming, p.232, 1998. ,
A comparative study of dierent color spaces for foreground and shadow detection for trac monitoring system, Proceedings of IEEE the 5th International Conference on Intelligent Transportation Systems, pp.100105-100117, 2002. ,
Online Adaptive Gaussian Mixture Learning for Video Applications, Workshop on Statistical Methods for Video Processing, p.105116, 2004. ,
DOI : 10.1109/TPAMI.2003.1233903
Eective gaussian mixture learning for video background subtraction, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, pp.827832-827850, 2005. ,
A texture-based approach for shadow detection, Proceedings. IEEE Conference on Advanced Video and Signal Based Surveillance, 2005., pp.371376-371389, 2005. ,
DOI : 10.1109/AVSS.2005.1577297
Gaussian-Based Codebook Model for Video Background Subtraction, International Conference on Natural Computation, p.25, 2006. ,
DOI : 10.1007/11881223_95
Segmentation of Moving Foreground Objects Using Codebook and Local Binary Patterns, 2008 Congress on Image and Signal Processing, pp.239243-239256, 2008. ,
DOI : 10.1109/CISP.2008.653
Automatic white balance for digital still camera, IEEE Transaction on Consumer Electronics, p.61, 1995. ,
Cast Shadow Removal Combining Local and Global Features, 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp.33-91, 2007. ,
DOI : 10.1109/CVPR.2007.383510
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.583.5541
An ecient method for detecting ghost and left objects in surveillance video, IEEE International Conference on Advanced Video and Signal based Surveillance, pp.540545-540585, 2007. ,
Painting with looks, Proceedings of the tenth ACM international conference on Multimedia , MULTIMEDIA '02, pp.117126-117170, 2002. ,
DOI : 10.1145/641007.641032
Learning and Removing Cast Shadows through a Multidistribution Approach, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, issue.37, pp.11331146-11331179, 2007. ,
Kernel-based learning of cast shadows from a physical model of light sources and surfaces for low-level segmentation, IEEE Conference on Computer Vision and Pattern Recognition, pp.18-33, 2008. ,
Learning Contextual Variations for Video Segmentation, Proceedings of the International Conference on Computer Vision System, pp.463473-463515, 2008. ,
DOI : 10.1007/978-3-540-79547-6_45
URL : https://hal.archives-ouvertes.fr/inria-00499631
Physical models for moving shadow and object detection in video, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.8, pp.10791087-10791119, 2004. ,
DOI : 10.1109/TPAMI.2004.51
Shadow Removal in Indoor Scenes, 2008 IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance, pp.34-91, 2008. ,
DOI : 10.1109/AVSS.2008.70
URL : https://hal.archives-ouvertes.fr/inria-00502953
Shadow flow: a recursive method to learn moving cast shadows, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, pp.891898-86, 2005. ,
DOI : 10.1109/ICCV.2005.217
Change detection by frequency decomposition:Wave-back, Proceedings of Workshop on Image Analysis for Multimedia Interactive Services, pp.84-204, 2005. ,
Understanding Background Mixture Models for Foreground Segmentation, Proceedings of Image and Vision Computing New Zealand, p.17, 2002. ,
Detecting moving shadows: algorithms and evaluation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.25, issue.7, pp.918923-918957, 2003. ,
DOI : 10.1109/TPAMI.2003.1206520
Color Space Selection for Moving Shadow Elimination, Fourth International Conference on Image and Graphics (ICIG 2007), pp.496501-496513, 2007. ,
DOI : 10.1109/ICIG.2007.54
Bayesian modeling of dynamic scenes for object detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, issue.11, pp.17781792-215, 2005. ,
Use of a real-time perception program supervisor in a driving scenario, Proceedings of the Intelligent Vehicles '94 Symposium, p.42, 1994. ,
DOI : 10.1109/IVS.1994.639545
Detection of moving cast shadows for object segmentation, IEEE Transaction on Multimedia, vol.1, pp.6576-56, 1999. ,
Adaptive background mixture models for real-time tracking, Proceedings IEEE Conference on Computer Vision and Pattern Recognition, pp.246252-246299, 0192. ,
The Focal Encyclopedia of Photography, Intelligent Vehicle Symposium, 1993. ,
Moving Object Segmentation Using Improved Running Gaussian Average Background Model. Computing: Techniques and Applications, DICTA '08.Digital Image, pp.2431-2464, 2008. ,
DOI : 10.1109/dicta.2008.15
A fast algorithm for adaptive background model construction using parzen density estimation, 2007 IEEE Conference on Advanced Video and Signal Based Surveillance, pp.528533-528556, 2007. ,
DOI : 10.1109/AVSS.2007.4425366
Automatic Robust Background Modeling Using Multivariate Non-parametric Kernel Density Estimation for Visual Surveillance, Proceedings of the International Symposium on Visual Computing, p.23, 2005. ,
DOI : 10.1007/11595755_44
Experience in Integrating Image Processing Programs, International Journal of Human-Computer Studies, Special Issue on Context, vol.138, pp.42-140, 1999. ,
Robust and ecient foreground analysis for real-time video surveillance, Proceedings IEEE Conference on Computer Vision and Pattern Recognition 2005, pp.11821187-11821200, 2005. ,
A Novel Shadow Detection Algorithm for Real Time Visual Surveillance Applications, 17th International Conference on Proceedings of the Pattern Recognition, pp.260263-98, 1997. ,
Wallower principles and practice of background maintenance, Proceeding of the International Conference on Computer Vision, ICCV 1999, p.255261, 1999. ,
Moving object, Time of day, Light switch, Waving trees, Camouage, Bootstrap and Foreground aperture ,
A probabilistic approach for foreground and shadow segmentation in monocular image sequences, Pattern Recognition, vol.38, issue.11, pp.19371946-19371979, 2005. ,
DOI : 10.1016/j.patcog.2005.02.006
Note on a Method for Calculating Corrected Sums of Squares and Products, Technometrics, vol.1, issue.1, pp.419420-209, 0200. ,
DOI : 10.1080/00401706.1962.10490022
Automatically tuning background subtraction parameters using particle swarm optimization, IEEE International Conference on Multimedia and Expo, pp.18261829-144, 2007. ,
Pnder : Realtime tracking of the human body, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.19, pp.780785-780799, 1997. ,