Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/90081
Title: A fast and self-adaptive on-line learning detection system
Authors: Prasad, Mukesh
Zheng, Ding-Rong
Mery, Domingo
Puthal, Deepak
Sundaram, Suresh
Lin, Chin-Teng
Keywords: On-line Learning
Object Detection
Engineering::Computer science and engineering
Issue Date: 2018
Source: 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
Series/Report no.: Procedia Computer Science
Abstract: 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.
URI: https://hdl.handle.net/10356/90081
http://hdl.handle.net/10220/49424
ISSN: 1877-0509
DOI: 10.1016/j.procs.2018.10.500
Rights: © 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/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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