Fast motion detection from airborne videos using graphics processing unit

In our previous work, we proposed a joint optical flow and principal component analysis (PCA) approach to improve the performance of optical flow based detection, where PCA is applied on the calculated two-dimensional optical flow image, and motion detection is accomplished by a metric derived from the two eigenvalues. To reduce the computational time when processing airborne videos, parallel computing using graphic processing unit (GPU) is implemented on NVIDIA GeForce GTX480. Experimental results demonstrate that our approach can efficiently improve detection performance even with dynamic background, and processing time can be greatly reduced with parallel computing on GPU.

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

[2]  K. P. Karmann,et al.  Moving object recognition using an adaptive background memory , 1990 .

[3]  Masaru Tanaka,et al.  Adaptive background estimation based on robust statistics , 2007, Systems and Computers in Japan.

[4]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[5]  Qian Du,et al.  Unsupervised Hyperspectral Band Selection Using Graphics Processing Units , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[6]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[7]  Qian Du,et al.  Optical Flow and Principal Component Analysis-Based Motion Detection in Outdoor Videos , 2010, EURASIP J. Adv. Signal Process..

[8]  Antonio Albiol,et al.  Fast motion detection in compressed domain for video surveillance , 2002 .

[9]  Martial Hebert,et al.  A Flow-Based Approach to Vehicle Detection and Background Mosaicking in Airborne Video , 2005, CVPR.

[10]  H. Hotelling.  Analysis of a complex of statistical variables into principal components. , 1933 .

[11]  Victor Podlozhnyuk,et al.  Image Convolution with CUDA , 2007 .

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

[13]  Wei Li,et al.  Fast Motion Detection Based on Accumulative Optical Flow and Double Background Model , 2005, CIS.

[14]  Robert T. Collins,et al.  Moving Object Localization in Thermal Imagery by Forward-backward MHI , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[15]  Xuelong Li,et al.  Vehicle detection and tracking in airborne videos by multi-motion layer analysis , 2011, Machine Vision and Applications.

[16]  Li Yang,et al.  An improved motion detection method for real-time surveillance , 2008 .

[17]  Tieniu Tan,et al.  A survey on visual surveillance of object motion and behaviors , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[18]  Karl Pearson F.R.S..  LIII. On lines and planes of closest fit to systems of points in space , 1901 .

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

[20]  P. KaewTrakulPong,et al.  An Improved Adaptive Background Mixture Model for Real-time Tracking with Shadow Detection , 2002 .

[21]  Berthold K. P. Horn,et al.  "Determining optical flow": A Retrospective , 1993, Artif. Intell..

[22]  Patrick Bouthemy,et al.  Computation and analysis of image motion: A synopsis of current problems and methods , 1996, International Journal of Computer Vision.

[23]  Mohan Malkani,et al.  Real-Time Multiple Moving Targets Detection from Airborne IR Imagery by Dynamic Gabor Filter and Dynamic Gaussian Detector , 2010, EURASIP J. Image Video Process..

[24]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[25]  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).

[26]  Chng Eng Siong,et al.  Foreground motion detection by difference-based spatial temporal entropy image , 2004, 2004 IEEE Region 10 Conference TENCON 2004..

[27]  Qian Du,et al.  A joint optical flow and principal component analysis approach for motion detection , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[28]  J. Weickert,et al.  Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods , 2005 .