MOVING TARGET DETECTION BASED ON GLOBAL MOTION ESTIMATION IN DYNAMIC ENVIRONMENT

AUV localization is not accurate based on sequence images if moving target is as landmark, so the moving target detection algorithm is studied based on global motion estimation, which detects and eliminates moving target according to the motion inconsistency of the moving target. Generally grid block matching is used in the global motion estimation, it can’t effectively dispose the dynamic background, and the gradient direction invariant moments descriptors method of free circular neighborhood based on feature points is proposed, which is effective for the background rotating and light changing in two adjacent images. For the matching points, the parameters of global motion are estimated robustly combined with normalized linear estimation method and least median squares. Experiments show that the designed algorithm can effectively estimate parameters of global motion, and eliminate the motion target as mismatch.

[1]  M. Manzur Murshed,et al.  A Fully Adaptive Distance-Dependent Thresholding Search (FADTS) Algorithm for Performance-Management Motion Estimation , 2007, IEEE Transactions on Circuits and Systems for Video Technology.

[2]  Mubarak Shah,et al.  COCOA: tracking in aerial imagery , 2006, SPIE Defense + Commercial Sensing.

[3]  Avinash C. Kak,et al.  Error analysis of robust optical flow estimation by least median of squares methods for the varying illumination model , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Peter Meer,et al.  Beyond RANSAC: User Independent Robust Regression , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[5]  Hai Tao,et al.  Object Tracking with Bayesian Estimation of Dynamic Layer Representations , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  L. Davis,et al.  Real-time multiple vehicle detection and tracking from a moving vehicle , 2000, Machine Vision and Applications.

[7]  S. M. Pandit,et al.  Automatic threshold selection based on histogram modes and a discriminant criterion , 1998, Machine Vision and Applications.

[8]  Robert Pless,et al.  Detecting Independent Motion: The Statistics of Temporal Continuity , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Yee-Hong Yang,et al.  The background primal sketch: An approach for tracking moving objects , 1992, Machine Vision and Applications.

[10]  Zhengyou Zhang,et al.  Determining the Epipolar Geometry and its Uncertainty: A Review , 1998, International Journal of Computer Vision.

[11]  S. N. Efstratiadis,et al.  Image Noise Reduction Based on Local Classification and Iterated Conditional Modes , 1996 .

[12]  Jong Bae Kim,et al.  Efficient region-based motion segmentation for a video monitoring system , 2003, Pattern Recognit. Lett..

[13]  Edward R. Dougherty,et al.  Mathematical methods for artificial intelligence and autonomous systems , 1988 .

[14]  Hironobu Fujiyoshi,et al.  Moving target classification and tracking from real-time video , 1998, Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV'98 (Cat. No.98EX201).

[15]  Aljoscha Smolic,et al.  Robust global motion estimation using a simplified M-estimator approach , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[16]  Philip H. S. Torr,et al.  The Development and Comparison of Robust Methods for Estimating the Fundamental Matrix , 1997, International Journal of Computer Vision.

[17]  Takeo Kanade,et al.  A System for Video Surveillance and Monitoring , 2000 .

[18]  Janusz Konrad,et al.  CHAPTER 3 – Motion Detection and Estimation , 2009 .

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

[20]  David J. Fleet,et al.  Performance of optical flow techniques , 1994, International Journal of Computer Vision.

[21]  Amir Averbuch,et al.  Fast motion estimation using bidirectional gradient methods , 2004, IEEE Trans. Image Process..

[22]  Sami S. Brandt Maximum Likelihood Robust Regression with Known and Unknown Residual Models , 2002 .