Realization of CUDA-based real-time registration and target localization for high-resolution video images

High-resolution video images contain huge amount of data so that the real-time capability of image registration and target localization algorithm is difficult to be achieved when operated on central processing units (CPU). In this paper, improved ORB (Oriented FAST and Rotated BRIEF, FAST, which means “Features from Accelerated Segment Test”, is a corner detection method used for feature points extraction. BRIEF means “Binary Robust Independent Elementary Features”, and it’s a binary bit string used to describe features) based real-time image registration and target localization algorithm for high-resolution video images is proposed. We focus on the parallelization of three of the most time-consuming parts: improved ORB feature extraction, feature matching based on Hamming distance for matching rough points, and Random Sample Consensus algorithm for precise matching and achieving transformation model parameters. Realizing Compute Unified Device Architecture (CUDA)-based real-time image registration and target localization parallel algorithm for high-resolution video images is also emphasized on. The experimental results show that when the registration and localization effect is similar, image registration and target localization algorithm for high-resolution video images achieved by CUDA is roughly 20 times faster than by CPU implementation, meeting the requirement of real-time processing.

[1]  Yizhen Jia,et al.  Rapid moving object detection algorithm based on ORB features: Rapid moving object detection algorithm based on ORB features , 2014 .

[2]  Zhang Zuxun.  Parallel Image Matching Algorithm Based on GPGPU , 2010 .

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

[4]  Pavel Zemcík,et al.  Real-time object detection on CUDA , 2010, Journal of Real-Time Image Processing.

[5]  Thomas Wiegand,et al.  SIFT Implementation and Optimization for General-Purpose GPU , 2007 .

[6]  Wang Hong-yuan,et al.  Fast Scale Invariant Feature Transform Algorithm Based on CUDA , 2010 .

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

[8]  Tom Drummond,et al.  Machine Learning for High-Speed Corner Detection , 2006, ECCV.

[9]  Vincent Lepetit,et al.  BRIEF: Binary Robust Independent Elementary Features , 2010, ECCV.

[10]  Gary R. Bradski,et al.  ORB: An efficient alternative to SIFT or SURF , 2011, 2011 International Conference on Computer Vision.

[11]  Yang Huang,et al.  Research on CUDA-Based SIFT Registration of SAR Image , 2011, 2011 Fourth International Symposium on Parallel Architectures, Algorithms and Programming.

[12]  Jan-Michael Frahm,et al.  Feature tracking and matching in video using programmable graphics hardware , 2007, Machine Vision and Applications.

[13]  Jie Xu,et al.  Fast feature-based video stabilization without accumulative global motion estimation , 2012, IEEE Transactions on Consumer Electronics.

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