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The Vehicle Tracking Research and Realization Based on the AIMM and Multi-Feature Integration

Author: ChenMeng
Tutor: WangXiaoMin
School: Southwest Jiaotong University
Course: Traffic and Transportation Engineering
Keywords: Vehicle Detection Adaptive background subtraction Vehicle Tracking AIMM-KF LabVIEW
CLC: TP391.41
Type: Master's thesis
Year: 2011
Downloads: 30
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Abstract

Based on the the video moving vehicle detection and tracking system is one of the important research content of the intelligent transportation system, it combines image processing, artificial intelligence, automatic control and other related areas of research, and a wide range of applications in the field of transportation and management. Therefore, the research to improve the performance of the intelligent transportation system based on the key technology in intelligent transportation systems in the video and has important theoretical significance and application value. In this paper, the main problems of vehicle detection and tracking in complex scenes, exclude interference, accurate tracking solutions and complex scenes. The main research work are as follows: pre-processing, image preprocessing, because there are all kinds of noise, captured video image grayscale equalization, median filtering and image processing technology, the video image so that the image quality can be improved, ready for the back of the vehicle detection and tracking. Moving vehicle detection, background subtraction method and inter-frame difference method is discussed and simulation, the inter-frame difference method is not well to extract all the characteristics of the background subtraction method is very sensitive to light changes and dynamic scene changes outside interference; pixel. In this paper, adaptive background subtraction method combined with image processing technology, did not increase under the premise of the calculation of the amount, and can adapt to a certain extent, the scene changes and be able to extract the full feature pixels, and then extracted the full movement of the target vehicle behind the vehicle feature extraction work to lay a solid foundation. Sports vehicle tracking using adaptive interactive multiple model Kalman filter to establish uniform, constant acceleration model, to forecast track vehicles that may arise in the next frame position to determine the search range, combined with this paper, feature matching algorithm to track the movement of vehicles turning occlusion. Finally, the tracking algorithm experimentation and simulation, and analysis of the experimental data. The test results show that the adaptive interactive multiple model-based Kalman filter vehicle tracking model more in line with the actual situation, can reliably predict and track the trajectory of the vehicle. Finally, using the LabVIEW development platform designed the AIMM and characteristics of fusion based vehicle tracking system. The demonstration system initially verify the feasibility of the algorithm.

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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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