Simplified multiple object tracking model for real-time intelligent surveillance system

In this paper, we propose detection based simplified multiple object tracking model with handling stationary object detection and occlusion problem for real-time intelligent surveillance system. In order to solve detection of slow and stationary object problem in Gaussian Mixture Model(GMM) based adaptive background model, we presents controlling learning rate mechanism using tracked region information. And, the simple primitive multi-features are applied for real-time multiple object tracking. As well, we proposed modified moving average filter for predicting next position of moving object to handle occlusion problems. Computational and real-target experiment results show that the proposed model can successfully track moving object within 45ms per frame for 640×480 image size on Intel® Core(TM) i7 CPU 1.6GHz in a real indoor scene including occlusion situation.

[1]  Quan Pan,et al.  Real-time multiple objects tracking with occlusion handling in dynamic scenes , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[2]  Max Lu,et al.  Robust and efficient foreground analysis in complex surveillance videos , 2012, Machine Vision and Applications.

[3]  Sharath Pankanti,et al.  Appearance models for occlusion handling , 2006, Image Vis. Comput..

[4]  Rita Cucchiara,et al.  Probabilistic people tracking with appearance models and occlusion classification: The AD-HOC system , 2011, Pattern Recognit. Lett..

[5]  Bir Bhanu,et al.  Physical models for moving shadow and object detection in video , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Ashley Tews,et al.  Real-Time Object Tracking and Classification Using a Static Came ra , 2009 .

[7]  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).

[8]  Larry S. Davis,et al.  Fast multiple object tracking via a hierarchical particle filter , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[9]  Fatih Murat Porikli,et al.  Shadow flow: a recursive method to learn moving cast shadows , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[10]  Alptekin Temizel,et al.  Adaptive mean-shift for automated multi object tracking , 2012 .

[11]  Lisa M. Brown,et al.  IBM smart surveillance system (S3): event based video surveillance system with an open and extensible framework , 2008, Machine Vision and Applications.

[12]  Thierry Bouwmans,et al.  Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey , 2008 .

[13]  S. Johnsen,et al.  Real-Time Object Tracking and Classification Using a Static Camera , 2009 .

[14]  Ferdinand van der Heijden,et al.  Efficient adaptive density estimation per image pixel for the task of background subtraction , 2006, Pattern Recognit. Lett..

[15]  James J. Little,et al.  Robust Visual Tracking for Multiple Targets , 2006, ECCV.