A fast and self-adaptive on-line learning detection system

This paper proposes a method to allow users to select target species for detection, generate an initial detection model by selecting a small piece of image sample and as the movie plays, continue training this detection model automatically. This method has noticeable detection results for several ty...

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Main Authors: Prasad, Mukesh, Zheng, Ding-Rong, Mery, Domingo, Puthal, Deepak, Sundaram, Suresh, Lin, Chin-Teng
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2019
Subjects:
Online Access:https://hdl.handle.net/10356/90081
http://hdl.handle.net/10220/49424
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-900812020-03-07T11:49:00Z A fast and self-adaptive on-line learning detection system Prasad, Mukesh Zheng, Ding-Rong Mery, Domingo Puthal, Deepak Sundaram, Suresh Lin, Chin-Teng School of Computer Science and Engineering On-line Learning Object Detection Engineering::Computer science and engineering This paper proposes a method to allow users to select target species for detection, generate an initial detection model by selecting a small piece of image sample and as the movie plays, continue training this detection model automatically. This method has noticeable detection results for several types of objects. The framework of this study is divided into two parts: the initial detection model and the online learning section. The detection model initialization phase use a sample size based on the proportion of users of the Haar-like features to generate a pool of features, which is used to train and select effective classifiers. Then, as the movie plays, the detecting model detects the new sample using the NN Classifier with positive and negative samples and the similarity model calculates new samples based on the fusion background model to calculate a new sample and detect the relative similarity to the target. From this relative similarity-based conservative classification of new samples, the conserved positive and negative samples classified by the video player are used for automatic online learning and training to continuously update the classifier. In this paper, the results of the test for different types of objects show the ability to detect the target by choosing a small number of samples and performing automatic online learning, effectively reducing the manpower needed to collect a large number of image samples and a large amount of time for training. The Experimental results also reveal good detection capability. Published version 2019-07-18T04:32:44Z 2019-12-06T17:40:15Z 2019-07-18T04:32:44Z 2019-12-06T17:40:15Z 2018 Journal Article Prasad, M., Zheng, D.-R., Mery, D., Puthal, D., Sundaram, S.,& Lin, C.-T. (2018). A fast and self-adaptive on-line learning detection system. Procedia Computer Science, 144, 13-22. doi:10.1016/j.procs.2018.10.500 1877-0509 https://hdl.handle.net/10356/90081 http://hdl.handle.net/10220/49424 10.1016/j.procs.2018.10.500 en Procedia Computer Science © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/). 10 p. application/pdf
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic On-line Learning
Object Detection
Engineering::Computer science and engineering
spellingShingle On-line Learning
Object Detection
Engineering::Computer science and engineering
Prasad, Mukesh
Zheng, Ding-Rong
Mery, Domingo
Puthal, Deepak
Sundaram, Suresh
Lin, Chin-Teng
A fast and self-adaptive on-line learning detection system
description This paper proposes a method to allow users to select target species for detection, generate an initial detection model by selecting a small piece of image sample and as the movie plays, continue training this detection model automatically. This method has noticeable detection results for several types of objects. The framework of this study is divided into two parts: the initial detection model and the online learning section. The detection model initialization phase use a sample size based on the proportion of users of the Haar-like features to generate a pool of features, which is used to train and select effective classifiers. Then, as the movie plays, the detecting model detects the new sample using the NN Classifier with positive and negative samples and the similarity model calculates new samples based on the fusion background model to calculate a new sample and detect the relative similarity to the target. From this relative similarity-based conservative classification of new samples, the conserved positive and negative samples classified by the video player are used for automatic online learning and training to continuously update the classifier. In this paper, the results of the test for different types of objects show the ability to detect the target by choosing a small number of samples and performing automatic online learning, effectively reducing the manpower needed to collect a large number of image samples and a large amount of time for training. The Experimental results also reveal good detection capability.
author2 School of Computer Science and Engineering
author_facet School of Computer Science and Engineering
Prasad, Mukesh
Zheng, Ding-Rong
Mery, Domingo
Puthal, Deepak
Sundaram, Suresh
Lin, Chin-Teng
format Article
author Prasad, Mukesh
Zheng, Ding-Rong
Mery, Domingo
Puthal, Deepak
Sundaram, Suresh
Lin, Chin-Teng
author_sort Prasad, Mukesh
title A fast and self-adaptive on-line learning detection system
title_short A fast and self-adaptive on-line learning detection system
title_full A fast and self-adaptive on-line learning detection system
title_fullStr A fast and self-adaptive on-line learning detection system
title_full_unstemmed A fast and self-adaptive on-line learning detection system
title_sort fast and self-adaptive on-line learning detection system
publishDate 2019
url https://hdl.handle.net/10356/90081
http://hdl.handle.net/10220/49424
_version_ 1681036961058390016