A robust object detection algorithm based on background difference and LK optical flow

A robust object detection algorithm based on background difference and LK (Lucas-Kanade, LK) optical flow is proposed. Firstly the object detection algorithms based on background difference and LK optical flow are improved respectively, and then these two algorithms are effectively fused with a novel fusion method, the problems caused by using single algorithm in practical application are solved. The experimental results show that the proposed fusion algorithm can effectively make up for the defects of using single algorithm, the performance of the moving object detection under complex scene is better than the other algorithms, the results show that the proposed algorithm is a robust and accurate object detection method.1.

[1]  F. Xavier Roca,et al.  Exploiting multiple cues in motion segmentation based on background subtraction , 2013, Neurocomputing.

[2]  Malin Premaratne,et al.  Hand gesture tracking and recognition system using Lucas-Kanade algorithms for control of consumer electronics , 2013, Neurocomputing.

[3]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[4]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[5]  Chaodong Fan,et al.  Small Probability Strategy Based Otsu Thresholding Method for Image Segmentation: Small Probability Strategy Based Otsu Thresholding Method for Image Segmentation , 2014 .

[6]  RothStefan,et al.  A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles Behind Them , 2014 .

[7]  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).

[8]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[9]  W Reichardt,et al.  Visual control of orientation behaviour in the fly: Part II. Towards the underlying neural interactions , 1976, Quarterly Reviews of Biophysics.

[10]  W. Eric L. Grimson,et al.  Learning Patterns of Activity Using Real-Time Tracking , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

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

[12]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[13]  Michael J. Black,et al.  A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles Behind Them , 2013, International Journal of Computer Vision.

[14]  Larry S. Davis,et al.  Background modeling and subtraction by codebook construction , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..