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Research on Tracking Algorithm for Moving Vehicles under Complex Environments

Author: ZengQingHong
Tutor: CaoJie
School: Lanzhou University of Technology
Course: Signal and Information Processing
Keywords: Target tracking Sports car tracking Particle filter Geometric active contour model Level set method
CLC: TP391.41
Type: Master's thesis
Year: 2011
Downloads: 59
Quote: 0
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Abstract


As a key technology of intelligent transportation systems, sports car tracking has become a vibrant field of computer vision research topic. How to give full play to the advantages of the sports car tracking technology, to maximize vehicle tracking performance, gradually become a research hotspot in recent years. However, the direction of the study is still in a relatively early stage, there are many difficulties in the practical application. First, when the moving vehicle tracking is used in the field of traffic monitoring and traffic management, the requirements of real-time processing of the video image sequence, but restricted to the existing computer software and hardware level, the requirements of the computational complexity for real-time tracking algorithm can not be too high; Secondly, Transport The scene has the diversity and complexity of processing algorithms need to take into account a variety of factors, the existing algorithm is too simple, can not achieve the desired result in the actual scene. Therefore, how to achieve robust tracking of moving vehicles under complex background to become a more realistic and challenging research topic. Complex context of the sports car tracking in-depth study on the current target tracking algorithm, discussed in detail, Snake model-based tracking and feature-based tracking algorithm based on particle filter tracking. Reasonable and effective improvements in the processing advantages and disadvantages of these algorithms for complex background based on their performance processing of complex background. The main research work are as follows: a comprehensive analysis of the standard particle filtering and geometric active contour model, particle filter algorithm particle update process is strictly dependent on the selection of parameters, and can not deal with the shortcomings of the curve topology changes proposed based on geometric active contour model particle filter, the geometric active contour model with the advantages of the particle filter combined technical processing object contour level set curve topology changes, improved resampling technique to increase the reliability of describing the target observation. 2, for complex scenes, single source of information tracking instability, resampling particle filter algorithm based on the multi-threaded layered. Particle filter tracking framework, sports features and contour features two clues to predict the likelihood model, with the movement, the use of geometric active contour model to update the particle and with stratified resampling method to overcome the particle degradation. Simulation results show that the proposed tracking algorithm is feasible. Based on geometric active contour model particle filter tracking algorithm capable of handling the curve topology changes, improve tracking accuracy; resampling particle filter algorithm based on multi-threaded layered with better tracking target is blocked and turning deformation under complex background results.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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