Dissertation > Excellent graduate degree dissertation topics show

Moving Human Objects Recognition Research in Conplex Traiffc Environment

Author: XuXiaPing
Tutor: JiangJiaFu
School: Changsha University of Science and Technology
Course: Applied Computer Technology
Keywords: moving human objects biomimetic pattern recognition moving objectsextraction contour feature high dimensional space covering fuzzyK-nearest neighbor recognition Adaboost
CLC: TP391.41
Type: Master's thesis
Year: 2012
Downloads: 100
Quote: 2
Read: Download Dissertation

Abstract


Multiple objects tracking and recognition technique with video image sequences incomplex traffic environment is one of the hot spots of intelligent traffic surveillance system inrecent years. It involves a wide range of technologies, including computer vision, imageprocessing and pattern recognition, artificial intelligence, communication etc. This papermainly focuses on multiple objects recognition, which is the important part of intelligentvideo surveillance system, and further research of moving human objects recognition inpassenger car video and related problems. After the comprehensive analysis of existing objectrecognition methods, this paper compares the traditional recognition methods and biomimeticpattern recognition(BPR) that starts with the way of knowing things like human beingsproposed by academician Wang Shoujue. Then, a novel moving human objects recognitionmethod of BPR based on high dimension space improved by fuzzy K-nearest neighborrecognition(FKNN) and another method bases on BPR and Adaboost are promoted, thesimulation experiment results verifies their efficiency and feasibility.Firstly, this paper studies the basic image processing techniques in complex trafficenvironment. Video image sequences preprocessing includes image de-noising and imageenhancement. Median filter and histogram equalization are chosen through the analysis andexperiments comparison of different methods. Then, it adopts the background subtractionwith improved adaptive Gaussian mixture model to deal with the dynamic scene changes incomplicated traffic environment and difficulties of moving objects extraction. In the stage ofmoving objects feature extraction, it presents common objects features in detail, especially themoving human body feature, and fused features of edge invariant moment, shape and gradientfeature of top view of human head contour image constitute the moving human objectscharacteristic values.To design human objects recognition algorithm, map the moving object feature into thehigh dimensional space according to BPR theory based on high dimensional space covering.Then analyze the distribution of moving human objects in the feature space to design triangleneuron for the moving objects covering implementation. A triangle neural network covering algorithm based on density selection is proposed to construct covering space. As theoverlapping space of different classes is easy to result in the error recognition, this paperemploys FKNN method to improve the further recognition. Experiment results indicate thatthe algorithm proposed this paper performs better than other methods, and it achieves highercorrect human objects recognition rate and reject rate, as well as reduce the false identification.On the other hand, it applies BPR into Adaboost algorithm for moving human objectsrecognition which has advantages both of Adaboost and BPR. Simulation results verify theeffectiveness of this method. That is to say, the two methods for moving human objects incomplex traffic environment both are effective and feasible.

Related Dissertations

  1. Research on the Classification Based on the Reconstruction of Solder Joint,TP391.41
  2. Tongue Feature Extraction and Research of Fusion Classification,TP391.41
  3. The Fatigue State Recognition of the Driver Based on Eye Detection,TP391.41
  4. Research on Text Classification Based on Biomimetic Pattern Recongnition,TP391.1
  5. Feature Extraction, Selection and Combination in Lipreading,TP391.41
  6. The Research on Paper Currency Classification Method Based on Harr-Like Feature and Minimal Ball Including Samples,TP391.41
  7. Research on Predicting Intrinsic Disorder Protein Structure Based on Supervision Manifold Learning Algorithm,Q51
  8. Face Recognition Method Based on DE,TP391.41
  9. The Design and Implementation of Face Detection Algorithm Based on FPGA Chip,TP391.41
  10. Face Detection and Implementation on DSP,TP391.41
  11. Design and Realization of Embedded Face Detection and Tracking System,TP274
  12. Research & Implementation of AdaBoost-Based Vehicle Recognition System,TP391.41
  13. The Reserch of Rapid Face Detection and Real-time Tracking Algorithms Based on Video Streaming,TP391.41
  14. Research on Face Detection System,TP391.41
  15. 3D Face Recognition Based on Biomimetic Pattern Recognition,TP391.41
  16. Research & Implementation of Face Detection Algorithm Based on Adaboost,TP391.41
  17. Face Detection Approaches Research Based on AdaBoost,TP391.41
  18. The Research of Human Face Detection and Tracking Algorithm in Video,TP391.41
  19. Research on Face Recognition Based on AdaBoost Algorithm,TP391.41
  20. Research and Implementation of Monocular Vision-Based Vehicle Detection Algorithm,TP274
  21. Research and Implementation on Multi-angle Face Detection Technology Based on Continuous Adaboost Algorithm,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