• Title/Summary/Keyword: Siamese 네트워크

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Trends on Visual Object Tracking Using Siamese Network (Siamese 네트워크 기반 영상 객체 추적 기술 동향)

  • Oh, J.;Lee, J.
    • Electronics and Telecommunications Trends
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    • v.37 no.1
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    • pp.73-83
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    • 2022
  • Visual object tracking can be utilized in various applications and has attracted considerable attention in the field of computer vision. Visual object tracking technology is classified in various ways based on the number of tracking objects and the methodologies employed for tracking algorithms. This report briefly introduces the visual object tracking challenge that contributes to the development of single object tracking technology. Furthermore, we review ten Siamese network-based algorithms that have attracted attention, owing to their high tracking speed (despite the use of neural networks). In addition, we discuss the prospects of the Siamese network-based object tracking algorithms.

Target Image Exchange Model for Object Tracking Based on Siamese Network (샴 네트워크 기반 객체 추적을 위한 표적 이미지 교환 모델)

  • Park, Sung-Jun;Kim, Gyu-Min;Hwang, Seung-Jun;Baek, Joong-Hwan
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.25 no.3
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    • pp.389-395
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    • 2021
  • In this paper, we propose a target image exchange model to improve performance of the object tracking algorithm based on a Siamese network. The object tracking algorithm based on the Siamese network tracks the object by finding the most similar part in the search image using only the target image specified in the first frame of the sequence. Since only the object of the first frame and the search image compare similarity, if tracking fails once, errors accumulate and drift in a part other than the tracked object occurs. Therefore, by designing a CNN(Convolutional Neural Network) based model, we check whether the tracking is progressing well, and the target image exchange timing is defined by using the score output from the Siamese network-based object tracking algorithm. The proposed model is evaluated the performance using the VOT-2018 dataset, and finally achieved an accuracy of 0.611 and a robustness of 22.816.

Object Tracking Algorithm based on Siamese Network with Local Overlap Confidence (지역 중첩 신뢰도가 적용된 샴 네트워크 기반 객체 추적 알고리즘)

  • Su-Chang Lim;Jong-Chan Kim
    • The Journal of the Korea institute of electronic communication sciences
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    • v.18 no.6
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    • pp.1109-1116
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    • 2023
  • Object tracking is used to track a goal in a video sequence by using coordinate information provided as annotation in the first frame of the video. In this paper, we propose a tracking algorithm that combines deep features and region inference modules to improve object tracking accuracy. In order to obtain sufficient object information, a convolution neural network was designed with a Siamese network structure. For object region inference, the region proposal network and overlapping confidence module were applied and used for tracking. The performance of the proposed tracking algorithm was evaluated using the Object Tracking Benchmark dataset, and it achieved 69.1% in the Success index and 89.3% in the Precision Metrics.

Host-Based Intrusion Detection Model Using Few-Shot Learning (Few-Shot Learning을 사용한 호스트 기반 침입 탐지 모델)

  • Park, DaeKyeong;Shin, DongIl;Shin, DongKyoo;Kim, Sangsoo
    • KIPS Transactions on Software and Data Engineering
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    • v.10 no.7
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    • pp.271-278
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    • 2021
  • As the current cyber attacks become more intelligent, the existing Intrusion Detection System is difficult for detecting intelligent attacks that deviate from the existing stored patterns. In an attempt to solve this, a model of a deep learning-based intrusion detection system that analyzes the pattern of intelligent attacks through data learning has emerged. Intrusion detection systems are divided into host-based and network-based depending on the installation location. Unlike network-based intrusion detection systems, host-based intrusion detection systems have the disadvantage of having to observe the inside and outside of the system as a whole. However, it has the advantage of being able to detect intrusions that cannot be detected by a network-based intrusion detection system. Therefore, in this study, we conducted a study on a host-based intrusion detection system. In order to evaluate and improve the performance of the host-based intrusion detection system model, we used the host-based Leipzig Intrusion Detection-Data Set (LID-DS) published in 2018. In the performance evaluation of the model using that data set, in order to confirm the similarity of each data and reconstructed to identify whether it is normal data or abnormal data, 1D vector data is converted to 3D image data. Also, the deep learning model has the drawback of having to re-learn every time a new cyber attack method is seen. In other words, it is not efficient because it takes a long time to learn a large amount of data. To solve this problem, this paper proposes the Siamese Convolutional Neural Network (Siamese-CNN) to use the Few-Shot Learning method that shows excellent performance by learning the little amount of data. Siamese-CNN determines whether the attacks are of the same type by the similarity score of each sample of cyber attacks converted into images. The accuracy was calculated using Few-Shot Learning technique, and the performance of Vanilla Convolutional Neural Network (Vanilla-CNN) and Siamese-CNN was compared to confirm the performance of Siamese-CNN. As a result of measuring Accuracy, Precision, Recall and F1-Score index, it was confirmed that the recall of the Siamese-CNN model proposed in this study was increased by about 6% from the Vanilla-CNN model.

Similarity Analysis Between SAR Target Images Based on Siamese Network (Siamese 네트워크 기반 SAR 표적영상 간 유사도 분석)

  • Park, Ji-Hoon
    • Journal of the Korea Institute of Military Science and Technology
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    • v.25 no.5
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    • pp.462-475
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    • 2022
  • Different from the field of electro-optical(EO) image analysis, there has been less interest in similarity metrics between synthetic aperture radar(SAR) target images. A reliable and objective similarity analysis for SAR target images is expected to enable the verification of the SAR measurement process or provide the guidelines of target CAD modeling that can be used for simulating realistic SAR target images. For this purpose, this paper presents a similarity analysis method based on the siamese network that quantifies the subjective assessment through the distance learning of similar and dissimilar SAR target image pairs. The proposed method is applied to MSTAR SAR target images of slightly different depression angles and the resultant metrics are compared and analyzed with qualitative evaluation. Since the image similarity is somewhat related to recognition performance, the capacity of the proposed method for target recognition is further checked experimentally with the confusion matrix.

Change Attention based Dense Siamese Network for Remote Sensing Change Detection (원격 탐사 변화 탐지를 위한 변화 주목 기반의 덴스 샴 네트워크)

  • Hwang, Gisu;Lee, Woo-Ju;Oh, Seoung-Jun
    • Journal of Broadcast Engineering
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    • v.26 no.1
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    • pp.14-25
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    • 2021
  • Change detection, which finds changes in remote sensing images of the same location captured at different times, is very important because it is used in various applications. However, registration errors, building displacement errors, and shadow errors cause false positives. To solve these problems, we propose a novle deep convolutional network called CADNet (Change Attention Dense Siamese Network). CADNet uses FPN (Feature Pyramid Network) to detect multi-scale changes, applies a Change Attention Module that attends to the changes, and uses DenseNet as a feature extractor to use feature maps that contain both low-level and high-level features for change detection. CADNet performance measured from the Precision, Recall, F1 side is 98.44%, 98.47%, 98.46% for WHU datasets and 90.72%, 91.89%, 91.30% for LEVIR-CD datasets. The results of this experiment show that CADNet can offer better performance than any other traditional change detection method.

Dense Siamese Network for Building Change Detection (건물 변화 탐지를 위한 덴스 샴 네트워크)

  • Hwang, Gisu;Lee, Woo-Ju;Oh, Seoung-Jun
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • 2020.07a
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    • pp.691-694
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    • 2020
  • 최근 원격 탐사 영상의 발달로 인해 작지만 중요한 객체에 대한 탐지 가능성이 커져 건물 변화 탐지에 대한 관심이 높아지고 있다. 본 논문은 건물 변화 탐지 방법 중 가장 좋은 성능을 가진 PGA-SiamNet 의 세부 변화 탐지의 정확도가 낮은 한계점을 개선시키기 위해 DensNet 기반의 Dense Siamese Network 를 제안한다. 제안하는 방법은 공개된 WHU 데이터 세트에 대해 변화 탐지 측정 지표인 TPR, OA, F1, Kappa 에 대해 97.02%, 99.5%, 97.44%, 97.16%의 성능을 얻었다. 기존 PGA-SiamNet 에 비해 TPR 은 0.83%, F1 은 0.02%, Kappa 는 0.02% 증가하였으며, 세부 변화 탐지의 성능이 우수함을 확인할 수 있다.

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The Sentence Similarity Measure Using Deep-Learning and Char2Vec (딥러닝과 Char2Vec을 이용한 문장 유사도 판별)

  • Lim, Geun-Young;Cho, Young-Bok
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.22 no.10
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    • pp.1300-1306
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    • 2018
  • The purpose of this study is to see possibility of Char2Vec as alternative of Word2Vec that most famous word embedding model in Sentence Similarity Measure Problem by Deep-Learning. In experiment, we used the Siamese Ma-LSTM recurrent neural network architecture for measure similarity two random sentences. Siamese Ma-LSTM model was implemented with tensorflow. We train each model with 200 epoch on gpu environment and it took about 20 hours. Then we compared Word2Vec based model training result with Char2Vec based model training result. as a result, model of based with Char2Vec that initialized random weight record 75.1% validation dataset accuracy and model of based with Word2Vec that pretrained with 3 million words and phrase record 71.6% validation dataset accuracy. so Char2Vec is suitable alternate of Word2Vec to optimize high system memory requirements problem.

Training of a Siamese Network to Build a Tracker without Using Tracking Labels (샴 네트워크를 사용하여 추적 레이블을 사용하지 않는 다중 객체 검출 및 추적기 학습에 관한 연구)

  • Kang, Jungyu;Song, Yoo-Seung;Min, Kyoung-Wook;Choi, Jeong Dan
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.21 no.5
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    • pp.274-286
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    • 2022
  • Multi-object tracking has been studied for a long time under computer vision and plays a critical role in applications such as autonomous driving and driving assistance. Multi-object tracking techniques generally consist of a detector that detects objects and a tracker that tracks the detected objects. Various publicly available datasets allow us to train a detector model without much effort. However, there are relatively few publicly available datasets for training a tracker model, and configuring own tracker datasets takes a long time compared to configuring detector datasets. Hence, the detector is often developed separately with a tracker module. However, the separated tracker should be adjusted whenever the former detector model is changed. This study proposes a system that can train a model that performs detection and tracking simultaneously using only the detector training datasets. In particular, a Siam network with augmentation is used to compose the detector and tracker. Experiments are conducted on public datasets to verify that the proposed algorithm can formulate a real-time multi-object tracker comparable to the state-of-the-art tracker models.