A object tracking method in two cameras with common view field

A object tracking method is proposed in this paper by utilizing the repetitive information in two cameras with common view field. First, the background will be built using Gaussian background modeling for the images of different cameras with common view field. Second, the foreground objects can be attained and extracted using the background subtraction method. SIFT(Scale-Invariant Feature Transform) feature points will be matched for the objects extracted from the images of the different cameras. Then RANSAC(RANdom SAmple Consensus) algorithm is used to filter the SIFT feature matching, and the same object of two images will be attained. The results demonstrate that this method is better for far field object from different cameras with common view field. It has potential and important applications in object matching and tracking of multicameras.

[1]  王伟 Wang Wei,et al.  Study on the Occlusion Problem in Dynamic Space Intersection Measurement with Multi-Camera Systems , 2014 .

[2]  Ren Jie,et al.  Real-time object detection with foreground fusion from multiple cameras using homography mapping of polygon vertices , 2016 .

[3]  明英 明英,et al.  Background Modeling and Moving-Objects Detection Based on Cauchy Distribution for Video Sequence , 2008 .

[4]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[5]  何雪东 He Xuedong,et al.  Fast Scale Adaptive Kernel Correlation Filtering Algorithm for Target Tracking , 2018 .

[6]  Yang Jie,et al.  INFRARED OBJECT TRACKING BASED ON PARTICLE FILTERS , 2006 .

[7]  Zhou Xu.  Moving object detection by combining SIFT and differential multiplication , 2011 .

[8]  Lin Hai.  Research on Object Tracking Algorithm Based on SIFT , 2010 .

[9]  Zhang Xiao-Ju.  Re-Identifying Targets Across Cameras in Frequency Domain , 2016 .

[10]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[11]  He Hong.  Moving Object Detection Based on Improved Mixture Gaussian Models , 2007 .

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

[13]  Jiang Gangyi,et al.  Review on Vehicle Detection and Tracking Techniques Based on Video Processing in Intelligent Transportation Systems , 2005 .