2021 International Conference on Multimedia Analysis and Pattern Recognition (MAPR)最新文献

An efficient method to improve the accuracy of Vietnamese vehicle license plate recognition in unconstrained environment 一种提高无约束环境下越南车牌识别精度的有效方法

2021 International Conference on Multimedia Analysis and Pattern Recognition (MAPR) Pub Date : 2021-10-01 DOI: 10.1109/MAPR53640.2021.9585279

Khanh Nguyen Quoc, Dan Pham Van, Van Pham Thi Bich

{"title":"An efficient method to improve the accuracy of Vietnamese vehicle license plate recognition in unconstrained environment","authors":"Khanh Nguyen Quoc, Dan Pham Van, Van Pham Thi Bich","doi":"10.1109/MAPR53640.2021.9585279","DOIUrl":"https://doi.org/10.1109/MAPR53640.2021.9585279","url":null,"abstract":"Background: Most previous studies in automatic license plate recognition (ALPR) focused on recognizing license plate (LP) in constrained environment where cameras are installed in front of LPs and other conditions such as lighting, weather, and image quality are satisfied. Besides, recent studies on ALPR in Vietnam have conducted in small datasets and have not covered various cases of Vietnamese LPs.Aim: To develop a model for ALPR that is effective in unconstrained environment in Vietnam.Method: We propose two improvements: We apply the idea of the key-point detection problem for LP detection part, and use a segmentation free approach based on encoder decoder network for the LP optical character recognition (OCR) part. We train and evaluate models in a large dataset collected from unconstrained environment.Results: Our results show improvements in LP detection accuracy with mean IOU mIOU = 95.01% and precision P75 = 99, 5%. The accuracy in LP OCR was up to Accseq = 99.28% at sequence level and Accchar = 99.7% at character level.Conclusion: We provide a large dataset of Vietnamese LP images that can be effectively used to evaluate ALPR systems in Vietnam, and proposes improvement techniques to tackle problems of ALPR in unconstrained environment in Vietnam.","PeriodicalId":233540,"journal":{"name":"2021 International Conference on Multimedia Analysis and Pattern Recognition (MAPR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122782576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Beauty Moment Rendering via Face Happiness Scoring 通过面部幸福评分的美丽时刻渲染

2021 International Conference on Multimedia Analysis and Pattern Recognition (MAPR) Pub Date : 2021-10-01 DOI: 10.1109/MAPR53640.2021.9585257

Duc-Tuan Luu, Qu Dong, Lam M. Nguyen, Ngoc-Khanh Nguyen, Tiep V. Nguyen, M. Tran

引用次数: 0

Multilingual-GAN: A Multilingual GAN-based Approach for Handwritten Generation 多语言gan:一种基于多语言gan的手写生成方法

2021 International Conference on Multimedia Analysis and Pattern Recognition (MAPR) Pub Date : 2021-10-01 DOI: 10.1109/MAPR53640.2021.9585285

Manh-Khanh Ngo Huu, Sy-Tuyen Ho, Vinh-Tiep Nguyen, T. Ngo

引用次数: 1

Data Hiding in Bit-plane Medical Image Using Chaos-based Steganography 基于混沌隐写技术的位平面医学图像数据隐藏

2021 International Conference on Multimedia Analysis and Pattern Recognition (MAPR) Pub Date : 2021-10-01 DOI: 10.1109/MAPR53640.2021.9585243

Ta Thi Kim Hue, N. T. Linh, Minh Nguyen-Duc, T. Hoang

引用次数: 1

A Unified Deep Framework for Hand Pose Estimation and Dynamic Hand Action Recognition from First-Person RGB Videos 基于第一人称RGB视频的手部姿态估计和动态手部动作识别的统一深度框架

2021 International Conference on Multimedia Analysis and Pattern Recognition (MAPR) Pub Date : 2021-10-01 DOI: 10.1109/MAPR53640.2021.9585280

Viet-Duc Le, Van-Nam Hoang, Tien Nguyen, Van-Hung Le, Thanh-Hai Tran, Hai Vu, Thi-Lan Le

{"title":"A Unified Deep Framework for Hand Pose Estimation and Dynamic Hand Action Recognition from First-Person RGB Videos","authors":"Viet-Duc Le, Van-Nam Hoang, Tien Nguyen, Van-Hung Le, Thanh-Hai Tran, Hai Vu, Thi-Lan Le","doi":"10.1109/MAPR53640.2021.9585280","DOIUrl":"https://doi.org/10.1109/MAPR53640.2021.9585280","url":null,"abstract":"Understanding hand action from the first-person video has emerged recently thanks to its wide potential applications such as hand rehabilitation, augmented reality. The majority of works mainly reply on RGB images. Compared with RGB images, hand joints have certain advantages as they are robust to illuminations and appearance variation. However, previous works for hand action recognition usually employed hand joints that are manually determined. This paper presents a unified framework for both hand pose estimation and hand action recognition from first-person RGB images. First, our framework estimates 3D hand joints from every RGB image using a combination of Resnet and a Graphical convolutional network. Then, an adaptation of a SOTA method PA-ResGCN for the human skeleton is proposed for hand action recognition from estimated hand joints. Our framework takes advantage of efficient graphical networks to model graph-like human hand structure in both phases: hand pose estimation and hand action recognition. We evaluate the proposed framework on the First Person Hand Action Benchmark (FPHAB). The experiments show that the proposed framework outperforms different SOTA methods on both hand pose estimation and hand action recognition tasks.","PeriodicalId":233540,"journal":{"name":"2021 International Conference on Multimedia Analysis and Pattern Recognition (MAPR)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115619811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Improvement of People Counting by Pairing Head and Face Detections from Still Images 基于静态图像中头部和面部配对检测的人计数改进

2021 International Conference on Multimedia Analysis and Pattern Recognition (MAPR) Pub Date : 2021-10-01 DOI: 10.1109/MAPR53640.2021.9585270

Thi-Oanh Ha, Hoang-Nhat Tran, Hong-Quan Nguyen, Thanh-Hai Tran, Phuong-Dung Nguyen, H. Doan, V. Nguyen, Hai Vu, Thi-Lan Le

{"title":"Improvement of People Counting by Pairing Head and Face Detections from Still Images","authors":"Thi-Oanh Ha, Hoang-Nhat Tran, Hong-Quan Nguyen, Thanh-Hai Tran, Phuong-Dung Nguyen, H. Doan, V. Nguyen, Hai Vu, Thi-Lan Le","doi":"10.1109/MAPR53640.2021.9585270","DOIUrl":"https://doi.org/10.1109/MAPR53640.2021.9585270","url":null,"abstract":"Video or image-based people counting in real-time has multiple applications in intelligent transportation, density estimation or class management, and so on. This problem is usually carried out by detecting people using conventional detectors. However, this approach can be failed when people stay in various postures or are occluded by each other. In this paper, we notice that even a main part of human body is occluded, their face and head are still observable. We then propose a method that counts people based on face and head detection and pairing. Instead of deploying only face or head detector, we apply both detectors as in many cases the human does not turn his/her face to camera then head detector takes advantage. Otherwise, face detector produces reliable results. The fact of combining both head and face detection results will lead to duplicated responses for one person. We then propose a simple yet effective alignment technique to pair a face with a head of a person. Subsequently, the remaining heads and faces which are not paired with any other faces or heads will be added to our people counter to increase the true positive rate. We evaluate our proposed method on four datasets (Hollywood, Casablanca, Wider Face, and our own dataset). The experimental results show an improvement of average precision and recall comparing to the original head or face detectors.","PeriodicalId":233540,"journal":{"name":"2021 International Conference on Multimedia Analysis and Pattern Recognition (MAPR)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122620951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Exploring Efficiency of GAN-based Generated URLs for Phishing URL Detection 网络钓鱼URL检测中基于gan生成URL的效率研究

2021 International Conference on Multimedia Analysis and Pattern Recognition (MAPR) Pub Date : 2021-10-01 DOI: 10.1109/MAPR53640.2021.9585287

T. Pham, Thi Thanh Thuy Pham, Sy Tuong Hoang, Viet-Cuong Ta

{"title":"Exploring Efficiency of GAN-based Generated URLs for Phishing URL Detection","authors":"T. Pham, Thi Thanh Thuy Pham, Sy Tuong Hoang, Viet-Cuong Ta","doi":"10.1109/MAPR53640.2021.9585287","DOIUrl":"https://doi.org/10.1109/MAPR53640.2021.9585287","url":null,"abstract":"The URL (Uniform Resource Locator) is used to refer to the resources on the Internet by giving hyperlinks to the websites. Different resources are referenced by different network addresses or different URLs. As a result, embedding malware on websites by using malicious URLs is one of the most dangerous types of cyberattacks today and poses a serious threats to the safety of systems. In order to detect the phishing URLs, the most commonly used approach recently is using deep learning networks with a large number of URL samples, including both malign and benign ones for training the deep networks. However, the available URL databases have a modest number of samples. In addition, the disadvantage of these databases is the imbalance distribution of malicious and non-malicious URL strings. In fact, it is difficult to collect or update malicious URLs because these URLs only exist for a short time, after being detected they are changed again and again. In order to solve this challenge, in this work, we propose to train a GAN network named WGAN-GP for generating malicious URLs from the available phishing URL data. We then integrate the generated phishing URL data into the existing URL database and perform two URL classifiers of LSTM and GRU to give the comparative results. The experiments on different quantities of URL samples show the improvement for URL classification by using WGAN-GP and LSTM classifier.","PeriodicalId":233540,"journal":{"name":"2021 International Conference on Multimedia Analysis and Pattern Recognition (MAPR)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114880944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

An improved spiking network conversion for image classification 一种用于图像分类的改进尖峰网络转换

2021 International Conference on Multimedia Analysis and Pattern Recognition (MAPR) Pub Date : 2021-10-01 DOI: 10.1109/MAPR53640.2021.9585199

Thu Quyen Nguyen, Q. Pham, Chi Hoang-Phuong, Quang Hieu Dang, Duc Minh Nguyen, Hoang Nguyen-Huy

{"title":"An improved spiking network conversion for image classification","authors":"Thu Quyen Nguyen, Q. Pham, Chi Hoang-Phuong, Quang Hieu Dang, Duc Minh Nguyen, Hoang Nguyen-Huy","doi":"10.1109/MAPR53640.2021.9585199","DOIUrl":"https://doi.org/10.1109/MAPR53640.2021.9585199","url":null,"abstract":"Image classification is always an interesting problem due to its practical applications in real life. With a capability of self-learning features, modern Convolution Neural Network (CNN) models can achieve high accuracy on large and complex benchmark datasets. However, due to their high computation costs, the CNN models experience energy consumption problems during training and implementation of the hardware which limits their utilisation in mobile and embedded applications. Recently, the Spiking Neural Network (SNN) has been proposed to overcome drawbacks of the CNN models. Like the biological nervous system, the SNN’s neurons communicate with each other by sending spike trains. A neuron is only calculated when a new input spike arrives. As a result, it turns the networks into an energy-saving mode which is suitable for implementation on hardware devices. To avoid the difficulty of the SNN direct training, an indirect training approach is proposed in this work. A proposed CNN model is firstly trained with the RMSprop algorithm then the optimised weights and bias are mapped to the SNN model converted from the proposed CNN model. Experimental results confirm that our model achieves the best accuracy of 93.5% when compared to state-of-the-art SNN approaches on the Fashion- MNIST dataset.","PeriodicalId":233540,"journal":{"name":"2021 International Conference on Multimedia Analysis and Pattern Recognition (MAPR)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122465175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Title Page 标题页

2021 International Conference on Multimedia Analysis and Pattern Recognition (MAPR) Pub Date : 2021-10-01 DOI: 10.1109/mapr53640.2021.9585291

引用次数: 0

A review of SNN implementation on FPGA SNN在FPGA上的实现综述

2021 International Conference on Multimedia Analysis and Pattern Recognition (MAPR) Pub Date : 2021-10-01 DOI: 10.1109/MAPR53640.2021.9585245

Quoc Trung Pham, Thu Quyen Nguyen, Chi Hoang-Phuong, Quang Hieu Dang, Duc Minh Nguyen, Hoang Nguyen-Huy

引用次数: 4