Dissertation > Excellent graduate degree dissertation topics show

The Detection of Target in Moving Background and the Implementation on Embedded System

Author: ZhangYa
Tutor: ShiJiuGen
School: Hefei University of Technology
Course: Computer Architecture
Keywords: Moving object detection Block matching Embedded systems Opticalflow Edge detection
CLC: TP391.41
Type: Master's thesis
Year: 2012
Downloads: 69
Quote: 0
Read: Download Dissertation

Abstract


Moving target detection technology is one of the key technologies of computervision system and an important foundation of the target tracking and behaviorrecognition. At present, the target detection technology in the static scene hasapplied in military, transportation, industrial manufacturing and other fieldsmaturely. Compare to target detection in the stationary background; target detectionin the motive scene is more complex, so the target detection in a static scene cannotbe directly applied to target detection in the moving scene. How to detect themoving targets in the motive background accurately is the difficulty of targetdetection study. In this dissertation, we analyze the shortcomings of the currentdetection algorithm, propose the improved algorithm, and design a new set of targetdetection system.Firstly, we summarize the definition, the calculation methods and the principleof optical flow algorithm in detail, especially detailed analysis and introduce themotion compensation and the search algorithm of the block matching optical flow.And we study the block-matching optical flow method how to apply to the programof moving background.Then, we discuss the defects and deficiencies of block-matching optical flowmethod applied to the moving background, and propose an improved algorithm: weput forward an improved search method of block-matching algorithm which isadded the block-matching offset probability as an additional weight value. Thisimproved method can not only searches for the point near the center of the offsetwith a high probability, but also takes the large area of the offset distance intoaccount, so the overall search efficiency is greatly improved. For the compensatedmotion vector, we introduce an enhanced Ostu algorithm to automatically obtainthreshold, which used for binarization processing in vector to separate thebackground motion vector and the target motion vector. We apply the improvedmedian filtering to Canny edge detection algorithm, make it applicable to andintegrate it to the proposed algorithm, and then extract the target contour of motionregions. The simulation results show that the improved algorithm of this article canaccurately detect the moving object in a moving background. Finally, this algorithm is ported to the embedded platform. We analyze the TI’sDaVinci embedded platform firstly, describe the advantage of its dual-core parallelprocessing technology in the field of audio and video applications, and then designa set of video capture, processing (target detection), codec functions as the movingobject detection system and achieve the algorithm program to this target detectionsystem. Because the platform does not support floating-point algorithm, we convertfloating-point to the vertex in the transplant process. According to thecharacteristics of the hardware platform, we also optimize the program in theprogram implementation process. We apply the improved optical flow algorithm tosystem we designed. The experimental results shows that the proposed algorithmcan meet the requirements of the moving object detection in a moving background,and verify the feasibility and correctness of the method proposed in thisdissertation.

Related Dissertations

  1. Research on Detecting Optical Fiber Geometric Parameter Based on Machine Vision,TN253
  2. Borehole imaging device based on embedded systems research,P634.3
  3. Research on Abnormal Vehicle Behavior Detection and Application in Traffic Video,TP391.41
  4. Research & Implementation of Moving Object Detection Based on Fish-eye Camera,TP391.41
  5. The Research and Implementation of Background Modeling and Updating Algorithm Based on Mixture Gaussian Model,TP391.41
  6. The Research and Development of the Bar Counting Based on Image Processing,TP391.41
  7. Line Extraction ,matching and Three-dimensional Reconstruction in Satellite Images,TP751
  8. Research and Realization of Image Denoising Based on Wavelet Transform,TP391.41
  9. ARM9-based Monitoring System for Aquaculture,TP368.1
  10. Based on the difference between the pixel grayscale image denoising wavelet edge detection and combining adjacent to its,TP391.41
  11. Study on Road Extraction of High Resolution Polarimetric SAR Interferometry Data,TN957.52
  12. The Research of Edge Detection Based on Mathematical Morphology,TP391.41
  13. Detection Algorithms on the Surface Edge Defects of Galvanized Sheet Based on Digital Image,TP274
  14. Research of Multi-scale Edge Detection Based on Mathematical Morphology,TP391.41
  15. Study of Plate Thickness Measurement System with Laser Illumination,TN247
  16. Research on Vision Detection Algorithm for Tracing Printing System,TP391.41
  17. Research and Realization of Vehicle License Plate Location and Slant Correction,TP391.41
  18. Research on Image Segmentation Method Based on Measure of Medium Truth Degree,TP391.41
  19. Research on Noise Reducation and Color Interpolation Algorithms Based on Bayer Pattern Images for Digital Camera,TP391.41
  20. Study of Binocular Stereo Matching Algorithm and Algorithm Implementation Based on Multi-core.,TP391.41
  21. Research on Image Super-resolution Reconstruction Based on Non-local Similarity,TP391.41

CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
© 2012 www.DissertationTopic.Net  Mobile