The Research of Video Tracking System Based on Virtual Reality

The paper introduces the video tracking simulation system with 3D virtual target system. The simulation has been used to the automatic target tracking system of chariots. And it describes a new method to detect and track moving objects in a dynamic scene based on background subtraction. We are especially careful of the core problem arising in the analysis of outdoor daylight scenes, continuous variations of lighting conditions that caused unexpected changes in intensities on the background reference image. Finally, experimental results and a performance measure establishing the confidence of the method are presented.

[1]  Nikos Paragios,et al.  Background modeling and subtraction of dynamic scenes , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[2]  Azriel Rosenfeld,et al.  Detection and location of people in video images using adaptive fusion of color and edge information , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[3]  HyunWook Park,et al.  Segmentation of forward-looking infrared image using fuzzy thresholding and edge detection , 2001 .

[4]  Alex Pentland,et al.  Pfinder: real-time tracking of the human body , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

[5]  J. Sklansky,et al.  Segmentation of people in motion , 1991, Proceedings of the IEEE Workshop on Visual Motion.

[6]  Alex Pentland,et al.  Pfinder: Real-Time Tracking of the Human Body , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Hsu-Yung Cheng,et al.  Motion detection via change-point detection for cumulative histograms of ratio images , 2005, Pattern Recognit. Lett..

[8]  I. S. Mackenzie,et al.  Virtual Environments and Advanced Interface Design , 1995 .

[9]  Carlos Orrite-Uruñuela,et al.  Detected motion classification with a double-background and a Neighborhood-based difference , 2003, Pattern Recognit. Lett..

[10]  Terrance E. Boult,et al.  Frame-rate omnidirectional surveillance and tracking of camouflaged and occluded targets , 1999, Proceedings Second IEEE Workshop on Visual Surveillance (VS'99) (Cat. No.98-89223).

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

[12]  Larry S. Davis,et al.  Non-parametric Model for Background Subtraction , 2000, ECCV.

[13]  Ralph Schroeder,et al.  Possible Worlds: The Social Dynamic of Virtual Reality Technology , 1996 .

[14]  Rita Cucchiara,et al.  Detecting objects, shadows and ghosts in video streams by exploiting color and motion information , 2001, Proceedings 11th International Conference on Image Analysis and Processing.

[15]  A. T. Ali,et al.  Visual road traffic monitoring and data collection , 1993, Proceedings of VNIS '93 - Vehicle Navigation and Information Systems Conference.

[16]  Tieniu Tan,et al.  Recent developments in human motion analysis , 2003, Pattern Recognit..

[17]  Stephen R. Ellis.  Origins and elements of virtual environments , 1995 .

[18]  D. Koller,et al.  Towards robust automatic traffic scene analysis in real-time , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[19]  Nikos Paragios,et al.  Motion-based background subtraction using adaptive kernel density estimation , 2004, CVPR 2004.

[20]  Xavier Maldague,et al.  Outdoor infrared video surveillance: A novel dynamic technique for the subtraction of a changing background of IR images , 2007 .