Vehicles Detection at Urban Intersections via Adaptive Neighbor Sets of Nonparametric Scene Parsing

碩士 === 國立清華大學 === 資訊工程學系所 === 105 === The challenges faced by many experiments with the vehicles detection at the urban intersections are that a bounding box is manually given to circle out the target object in the first frame and that the lost target object during a procedure of tracking might lead...

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Main Authors: Shih, Yi-Hsuan, 施亦宣
Other Authors: Wang, Jia-Shung
Format: Others
Language:en_US
Published: 2017
Online Access:http://ndltd.ncl.edu.tw/handle/2rpj85
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spelling ndltd-TW-105NTHU53920842019-05-15T23:53:47Z http://ndltd.ncl.edu.tw/handle/2rpj85 Vehicles Detection at Urban Intersections via Adaptive Neighbor Sets of Nonparametric Scene Parsing 以最適鄰近集之非參數場景剖析技術應用於偵測十字路口之車輛 Shih, Yi-Hsuan 施亦宣 碩士 國立清華大學 資訊工程學系所 105 The challenges faced by many experiments with the vehicles detection at the urban intersections are that a bounding box is manually given to circle out the target object in the first frame and that the lost target object during a procedure of tracking might lead to tracking error. Hence, the specific objective of this thesis is to explore some solutions to these problems. We apply the nonparametric scene parsing method to the vehicles detection at the urban intersections to automatically find out the car and motorcycle objects in the first frame without manually giving a bounding box. Moreover, the annotation results of scene parsing can improve the lost object. Many researches about the nonparametric scene parsing have been studied currently. The nonparametric scene parsing is a method to annotate a query image by transferring labels from the training data set. Referring to the method of [5], our proposed method firstly segments the images into superpixels. By means of calculating features, we can extract similar image set as the retrieval set from the training data set. In addition, we learn weights for each image in the training data set to minimize classification error using a leave-one-out strategy. In order to boost the classification of rare classes, we compute the semantic context of segments in the training data set and add the nearest rare class examples into the retrieval set. Finally, we compute the energy function in Markov Random Field (MRF) to label the query image. Since the scene of urban intersections is our main testing data set, we use background subtraction to extract foregrounds so as to reduce classification error. Our experimental results show that combination with the nonparametric scene parsing and background subtraction can effectively solve the problems of the vehicles detection at the urban intersections. Wang, Jia-Shung 王家祥 2017 學位論文 ; thesis 51 en_US
collection NDLTD
language en_US
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sources NDLTD
description 碩士 === 國立清華大學 === 資訊工程學系所 === 105 === The challenges faced by many experiments with the vehicles detection at the urban intersections are that a bounding box is manually given to circle out the target object in the first frame and that the lost target object during a procedure of tracking might lead to tracking error. Hence, the specific objective of this thesis is to explore some solutions to these problems. We apply the nonparametric scene parsing method to the vehicles detection at the urban intersections to automatically find out the car and motorcycle objects in the first frame without manually giving a bounding box. Moreover, the annotation results of scene parsing can improve the lost object. Many researches about the nonparametric scene parsing have been studied currently. The nonparametric scene parsing is a method to annotate a query image by transferring labels from the training data set. Referring to the method of [5], our proposed method firstly segments the images into superpixels. By means of calculating features, we can extract similar image set as the retrieval set from the training data set. In addition, we learn weights for each image in the training data set to minimize classification error using a leave-one-out strategy. In order to boost the classification of rare classes, we compute the semantic context of segments in the training data set and add the nearest rare class examples into the retrieval set. Finally, we compute the energy function in Markov Random Field (MRF) to label the query image. Since the scene of urban intersections is our main testing data set, we use background subtraction to extract foregrounds so as to reduce classification error. Our experimental results show that combination with the nonparametric scene parsing and background subtraction can effectively solve the problems of the vehicles detection at the urban intersections.
author2 Wang, Jia-Shung
author_facet Wang, Jia-Shung
Shih, Yi-Hsuan
施亦宣
author Shih, Yi-Hsuan
施亦宣
spellingShingle Shih, Yi-Hsuan
施亦宣
Vehicles Detection at Urban Intersections via Adaptive Neighbor Sets of Nonparametric Scene Parsing
author_sort Shih, Yi-Hsuan
title Vehicles Detection at Urban Intersections via Adaptive Neighbor Sets of Nonparametric Scene Parsing
title_short Vehicles Detection at Urban Intersections via Adaptive Neighbor Sets of Nonparametric Scene Parsing
title_full Vehicles Detection at Urban Intersections via Adaptive Neighbor Sets of Nonparametric Scene Parsing
title_fullStr Vehicles Detection at Urban Intersections via Adaptive Neighbor Sets of Nonparametric Scene Parsing
title_full_unstemmed Vehicles Detection at Urban Intersections via Adaptive Neighbor Sets of Nonparametric Scene Parsing
title_sort vehicles detection at urban intersections via adaptive neighbor sets of nonparametric scene parsing
publishDate 2017
url http://ndltd.ncl.edu.tw/handle/2rpj85
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