Shadow removal for pedestrian detection and tracking in indoor environments

This paper presents a method of shadow removal to improve the accuracy of pedestrian detection and tracking in indoor environments. The proposed method can be divided into four steps: building a background model which can be automatically updated, extract moving objects region, eliminating moving objects shadows, classifying and track pedestrians. The background model is built with pixel value and the updating of Gussian. The approach for real time background-foreground extraction is used to extract pedestrian region that may contains multiple shadows. In the gray histogram space, based on the depth value of the gray images, a reasonable threshold is set to remove shadows from various pedestrians. In this work, we propose a methodology using the foreground frames without shadows to detect and track the pedestrians across training datasets. Comparative experimental results show that our method is capable of dealing with shadows and detecting moving pedestrians in cluttered environments.

[1]  Te-Feng Su,et al.  Detecting moving objects from dynamic background with shadow removal , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[2]  Tomaso A. Poggio,et al.  Example-Based Object Detection in Images by Components , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Yunyi Liu,et al.  The Improved Moving Object Detection and Shadow Removing Algorithms for Video Surveillance , 2010, 2010 International Conference on Computational Intelligence and Software Engineering.

[4]  Tao Gao,et al.  Moving object detection with background subtraction and shadow removal , 2012, 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery.

[5]  Lucia Maddalena,et al.  A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications , 2008, IEEE Transactions on Image Processing.

[6]  Joongkyu Kim,et al.  Vehicle detection based on the use of shadow region and edge , 2013, Other Conferences.

[7]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[8]  Pietro Perona,et al.  Pedestrian Detection: An Evaluation of the State of the Art , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Jun Kong,et al.  A Novel Moving Cast Shadow Detection of Vehicles in Traffic Scene , 2012, IScIDE.

[10]  Philip Kelly.  Pedestrian detection and tracking using stereo vision techniques , 2008 .

[11]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[12]  Pei Ji.  ADAPTIVE MULTI THRESHOLDS IMAGE SEGMENTATION BASED ON POTENTIAL FUNCTION CLUSTERING , 1999 .

[13]  Rama Chellappa,et al.  Generalized Dictionaries for Multiple Instance Learning , 2015, International Journal of Computer Vision.

[14]  Chandrika Kamath,et al.  Robust techniques for background subtraction in urban traffic video , 2004, IS&T/SPIE Electronic Imaging.

[15]  Dariu Gavrila,et al.  Pedestrian Detection and Tracking Using a Mixture of View-Based Shape–Texture Models , 2008, IEEE Transactions on Intelligent Transportation Systems.

[16]  Hong Liu,et al.  A Fast and Robust Pedestrian Detection Framework Based on Static and Dynamic Information , 2012, 2012 IEEE International Conference on Multimedia and Expo.

[17]  Kardi Teknomo,et al.  Automation of Pedestrian Tracking in a Crowded Situation , 2016, ArXiv.

[18]  Jake K. Aggarwal,et al.  Human Shadow Removal with Unknown Light Source , 2010, 2010 20th International Conference on Pattern Recognition.

[19]  H. D. Cheng,et al.  Thresholding using two-dimensional histogram and fuzzy entropy principle , 2000, IEEE Trans. Image Process..

[20]  Qixiang Ye,et al.  Human Detection in Images via Piecewise Linear Support Vector Machines , 2013, IEEE Transactions on Image Processing.

[21]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[22]  Montse Pardàs,et al.  Shadow removal with blob-based morphological reconstruction for error correction , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[23]  Lie Guo,et al.  Study on pedestrian detection and tracking with monocular vision , 2010, 2010 2nd International Conference on Computer Technology and Development.

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

[25]  Gang Song,et al.  Object Detection Combining Recognition and Segmentation , 2007, ACCV.

[26]  Lina Sun,et al.  A shadow elimination method based on color and texture , 2010, 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems.

[27]  Edward Jones,et al.  Night-time pedestrian classification with histograms of oriented gradients-local binary patterns vectors , 2015 .

[28]  Jun-Wei Hsieh,et al.  Shadow elimination for effective moving object detection by Gaussian shadow modeling , 2003, Image Vis. Comput..

[29]  Thomas B. Moeslund,et al.  Detection and removal of chromatic moving shadows in surveillance scenarios , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[30]  Baisheng Chen.  Indoor and outdoor people detection and shadow suppression by exploiting HSV color information , 2008 .

[31]  Sei-Wang Chen,et al.  Shadow detection and removal for traffic images , 2004, IEEE International Conference on Networking, Sensing and Control, 2004.

[32]  Chin-Chen Chang,et al.  Brain Tumor Detection Using Color-Based K-Means Clustering Segmentation , 2007, Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2007).

[33]  Chia-Chen Lin,et al.  Brain Tumor Detection Using Color-Based K-Means Clustering Segmentation , 2007 .

[34]  Bo Wu,et al.  Pedestrian Tracking by Associating Tracklets using Detection Residuals , 2008, 2008 IEEE Workshop on Motion and video Computing.

[35]  Luc Van Gool,et al.  The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.

[36]  Jin Young Choi,et al.  Adaptive shadow estimator for removing shadow of moving object , 2010, Comput. Vis. Image Underst..

[37]  D.M. Gavrila,et al.  Vision-based pedestrian detection: the PROTECTOR system , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[38]  Yunqi Lei,et al.  Indoor and outdoor people detection and shadow suppression by exploiting HSV color information , 2004, The Fourth International Conference onComputer and Information Technology, 2004. CIT '04..

[39]  Alessandro Leone,et al.  A shadow elimination approach in video-surveillance context , 2006, Pattern Recognit. Lett..