2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)最新文献_第3页

Deep Face Image Retrieval for Cancelable Biometric Authentication 可取消生物特征认证的深度人脸图像检索

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-09-01 DOI: 10.1109/AVSS.2019.8909878

Young Kyun Jang, N. Cho

{"title":"Deep Face Image Retrieval for Cancelable Biometric Authentication","authors":"Young Kyun Jang, N. Cho","doi":"10.1109/AVSS.2019.8909878","DOIUrl":"https://doi.org/10.1109/AVSS.2019.8909878","url":null,"abstract":"This paper presents a cancelable biometric system for face authentication by exploiting the convolutional neural network (CNN)-based face image retrieval system. For the cancelable biometrics we must build a template that achieves good performance while maintaining some essential conditions. First the same template should not be used in different applications. Second if the compromise event occurs original biometric data should not be retrieved from the template. Last the template should be easily discarded and recreated. Hence we propose a Deep Table-based Hashing (DTH) framework that encodes CNN-based features into a binary code by utilizing the index of the hashing table. We employ noise embedding and intra-normalization that distorts biometric data which enhances the non-invertibility. For training we propose a new segment-clustering loss and pairwise Hamming loss with two classification losses. The final authentication results are obtained by voting on the outcome of the retrieval system. Experiments conducted on two large scale face image datasets demonstrate that the proposed method works as a proper cancelable biometric system.","PeriodicalId":243194,"journal":{"name":"2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133139573","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: 10

Exemplar-Based Pseudo-Viewpoint Rotation for White-Cane User Recognition from a 2D Human Pose Sequence 基于示例的伪视点旋转，用于二维人体姿态序列的白手杖用户识别

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-09-01 DOI: 10.1109/AVSS.2019.8909825

Naoki Nishida, Yasutomo Kawanishi, Daisuke Deguchi, I. Ide, H. Murase, Jun Piao

{"title":"Exemplar-Based Pseudo-Viewpoint Rotation for White-Cane User Recognition from a 2D Human Pose Sequence","authors":"Naoki Nishida, Yasutomo Kawanishi, Daisuke Deguchi, I. Ide, H. Murase, Jun Piao","doi":"10.1109/AVSS.2019.8909825","DOIUrl":"https://doi.org/10.1109/AVSS.2019.8909825","url":null,"abstract":"In recent years, various facilities are equipped to support visually impaired people, but accidents caused by visual disabilities still occur. In this paper, to support the visually-impaired people in a public space, we aim to classify whether a pedestrian image sequence obtained by a surveillance camera is a white-cane user or not from the temporal transition of a human pose represented as 2D coordinates. However, since the appearance of the 2D pose varies largely depending on the viewpoint of the pose, it is difficult to classify them. So, in this paper, we propose a method to rotate the viewpoint of a pose from various pseudo-viewpoints based on a pair of 2D poses simultaneously observed and classify the sequence by multiple classifiers corresponding to each viewpoint. Viewpoint rotation makes it possible to obtain pseudo-poses seen from various pseudo-viewpoints, extract richer pose features, and recognize white-cane users more accurately. Through an experiment, we confirmed that the proposed method improves the recognition rate by 12% compared to the method not employing viewpoint rotation.","PeriodicalId":243194,"journal":{"name":"2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114542074","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: 2

Real-Time Traffic Analysis using Deep Learning Techniques and UAV based Video 基于深度学习技术和无人机视频的实时交通分析

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-09-01 DOI: 10.1109/AVSS.2019.8909879

Huaizhong Zhang, Mark Liptrott, Nikolaos Bessis, Jianquan Cheng

引用次数: 25

SSSNet: Small-Scale-Aware Siamese Network for Gastric Cancer Detection SSSNet:用于胃癌检测的小规模感知暹罗网络

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-09-01 DOI: 10.1109/AVSS.2019.8909849

Chih-Chung Hsu, Hsin-Ti Ma, Jun-Yi Lee

{"title":"SSSNet: Small-Scale-Aware Siamese Network for Gastric Cancer Detection","authors":"Chih-Chung Hsu, Hsin-Ti Ma, Jun-Yi Lee","doi":"10.1109/AVSS.2019.8909849","DOIUrl":"https://doi.org/10.1109/AVSS.2019.8909849","url":null,"abstract":"In recent years, deep neural networks have become the most powerful supervised learning method. Several advanced neural networks, such as AlexNet, ZFNet, Inception, ResNet, and DenseNet, have achieved excellent performance on image recognition tasks. However, deep neural networks rely heavily on huge training sets to obtain good performance. Many applications, such as medical image analysis, do not allow for such large training sets, and it is difficult to train such networks on small-scale training sets. Magnifying narrow band imaging (M-NBI) is widely used to assist doctors in diagnosing gastric cancer, but relatively few of these images are available, compared with the number of general images. In this paper, we propose to use a Siamese network architecture to learn discriminative feature representations based on pairs of images. Then, we use a micro neural network to recognize these features and classify the input images. Our experimental results show that the proposed network can effectively learn discriminative features from a limited number of training images, and also that it can successfully recognize gastric cancer in M-NBI images.","PeriodicalId":243194,"journal":{"name":"2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116257294","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

Spatio-Temporal Semantic Segmentation for Drone Detection 无人机检测的时空语义分割

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-09-01 DOI: 10.1109/AVSS.2019.8909854

Céline Craye, Salem Ardjoune

{"title":"Spatio-Temporal Semantic Segmentation for Drone Detection","authors":"Céline Craye, Salem Ardjoune","doi":"10.1109/AVSS.2019.8909854","DOIUrl":"https://doi.org/10.1109/AVSS.2019.8909854","url":null,"abstract":"The democratization of drones over the past decade has opened wide cracks in airspace security. Research in drone detection and neutralization for critical infrastructures is a very active area with a number of open issues, such as robust detection of drones based on opto-electronic imaging. Indeed, drones at a certain distance only represent a few pixel points on an image, even on a high resolution camera, and can be easily mistaken for birds or any other flying objects in the airspace. In this context, we propose a spatio-temporal semantic segmentation approach based on convolutional neural networks. We handle the problem of detecting very small targets by using a U-Net architecture to identify areas of interest within the larger image. Then, we use a classification network, ResNet, to determine whether those areas contain a drone or not. To further help the localization and classification process, we provide spatiotemporal input patches to our networks. Drones are mostly moving targets, and birds do not follow the same kinds of trajectories; therefore, this additional feature significantly increases overall performance. This work was carried out in the context of the 2019 Drone-vs-Bird detection Challenge. The evaluation is conducted on the provided dataset under several configurations.","PeriodicalId":243194,"journal":{"name":"2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124110719","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: 39

High Efficient Single-stage Steel Surface Defect Detection 高效的单级钢表面缺陷检测

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-09-01 DOI: 10.1109/AVSS.2019.8909834

Fityanul Akhyar, Chih-Yang Lin, K. Muchtar, Tung-Ying Wu, Hui-Fuang Ng

{"title":"High Efficient Single-stage Steel Surface Defect Detection","authors":"Fityanul Akhyar, Chih-Yang Lin, K. Muchtar, Tung-Ying Wu, Hui-Fuang Ng","doi":"10.1109/AVSS.2019.8909834","DOIUrl":"https://doi.org/10.1109/AVSS.2019.8909834","url":null,"abstract":"To date, deep learning has been widely introduced in many fields, including object detection, medical imaging, and automation. One important application that uses deep learning based object detection is detecting defects by simply evaluating the image of an object. Such systems must be accurate, robust and efficient. Single-stage and two-stage object detection are two main approaches used in defect detection systems. A revised version of the popular object detection method called single shot multi-box detector (SSD) and the residual network (ResNet) offer a two-stage method to automatically detect defects with higher precision but has shown room for improvement with regard to speed performance. Therefore, in this paper, we propose a fully automatic pipeline for detecting defects, especially on steel surfaces. A novel transformation of the two-stage defect detection process into a more efficient single-stage detection process was introduced by utilizing a state-of-the-art method called RetinaNet. In addition, we leverage a feature pyramid network (FPN) and focal loss optimization to solve the small object detection problem and to deal with imbalanced background-foreground samples issue, respectively. Experimental results show that the proposed single-stage pipeline can achieve high accuracy and faster speed in steel surface defect detection.","PeriodicalId":243194,"journal":{"name":"2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124589094","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: 6

Person Head Detection Based Deep Model for People Counting in Sports Videos 基于人头检测的体育视频人数统计深度模型

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-09-01 DOI: 10.1109/AVSS.2019.8909898

Sultan Daud Khan, H. Ullah, M. Ullah, N. Conci, F. A. Cheikh, Azeddine Beghdadi

{"title":"Person Head Detection Based Deep Model for People Counting in Sports Videos","authors":"Sultan Daud Khan, H. Ullah, M. Ullah, N. Conci, F. A. Cheikh, Azeddine Beghdadi","doi":"10.1109/AVSS.2019.8909898","DOIUrl":"https://doi.org/10.1109/AVSS.2019.8909898","url":null,"abstract":"People counting in sports venues is emerging as a new domain in the field of video surveillance. People counting in these venues faces many key challenges, such as severe occlusions, few pixels per head, and significant variations in person's head sizes due to wide sport areas. We propose a deep model based method, which works as a head detector and takes into consideration the scale variations of heads in videos. Our method is based on the notion that head is the most visible part in the sports venues where large number of people are gathered. To cope with the problem of different scales, we generate scale aware head proposals based on scale map. Scale aware proposals are then fed to the Convolutional Neural Network (CNN) and it provides a response matrix containing the presence probabilities of people observed across scene scales. We then use non-maximal suppression to get the accurate head positions. For the performance evaluation, we carry out extensive experiments on two standard datasets and compare the results with state-of-the-art (SoA) methods. The results in terms of Average Precision (AvP), Average Recall (AvR), and Average F1-Score (AvF-Score) show that our method is better than SoA methods.","PeriodicalId":243194,"journal":{"name":"2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127814491","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: 21

An Interactive Framework for Cross-modal Attribute-based Person Retrieval 基于跨模态属性的人物检索交互式框架

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-09-01 DOI: 10.1109/AVSS.2019.8909832

Andreas Specker, Arne Schumann, J. Beyerer

引用次数: 3

Crowd Behavior Characterization for Scene Tracking 场景跟踪中的人群行为表征

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-09-01 DOI: 10.1109/AVSS.2019.8909893

G. Franchi, Emanuel Aldea, Séverine Dubuisson, I. Bloch

引用次数: 1

3D Gait Recognition Based on a CNN-LSTM Network with the Fusion of SkeGEI and DA Features 基于SkeGEI和DA特征融合的CNN-LSTM网络的三维步态识别

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-09-01 DOI: 10.1109/AVSS.2019.8909881

Yu Liu, Xinghao Jiang, Tanfeng Sun, Ke Xu

引用次数: 18