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Proceedings of the 6th International Conference on Robotics and Artificial Intelligence最新文献

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Perceptual Model based Surface Tessellation for VR Foveated Rendering 基于感知模型的VR注视点渲染曲面镶嵌
Proceedings of the 6th International Conference on Robotics and Artificial Intelligence Pub Date : 2020-11-20 DOI: 10.1145/3449301.3449313
Zipeng Zheng, Wenjun Cao, Wenjian Zhou, Zhuo Yang, Yinwei Zhan
{"title":"Perceptual Model based Surface Tessellation for VR Foveated Rendering","authors":"Zipeng Zheng, Wenjun Cao, Wenjian Zhou, Zhuo Yang, Yinwei Zhan","doi":"10.1145/3449301.3449313","DOIUrl":"https://doi.org/10.1145/3449301.3449313","url":null,"abstract":"With the rapid development of VR head mounted display, VR scenes need to be presented at higher resolutions and higher frame rates. Foveated rendering is an important technique to balance computing resource demand and user satisfaction. In this paper, we propose a perceptual model based foveated rendering. By culling the imperceptible region and adaptively adjusting the tessellation levels based on visual sensitivity, we improve the rendering performance in real-time rendering frameworks.","PeriodicalId":429684,"journal":{"name":"Proceedings of the 6th International Conference on Robotics and Artificial Intelligence","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127495081","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
Access Control Schema for Smart Locks using a Wifi Bridge: An exploration of a smart lock access control system based around the SimSim retrofitting smart lock. 基于Wifi网桥的智能锁访问控制方案:基于SimSim改造智能锁的智能锁访问控制系统探索。
Proceedings of the 6th International Conference on Robotics and Artificial Intelligence Pub Date : 2020-11-20 DOI: 10.1145/3449301.3449331
Andrew Zhang, Raghavendra V. P. Kandubai
{"title":"Access Control Schema for Smart Locks using a Wifi Bridge: An exploration of a smart lock access control system based around the SimSim retrofitting smart lock.","authors":"Andrew Zhang, Raghavendra V. P. Kandubai","doi":"10.1145/3449301.3449331","DOIUrl":"https://doi.org/10.1145/3449301.3449331","url":null,"abstract":"This paper presents an access control schema using a WIFI bridge for smart locks and explores its implementation in one such pair of devices. The schema outlines the interaction between a smart lock (SimSim), accompanying WIFI bridge (Freedom), a cloud server, and a group of users operating smartphones. The schema leverages AES256 symmetric encryption, Bluetooth Low Energy, and HTTPS communications to provide enhanced access control for short-term landlords participating in the sharing economy. These additional features include facilitating a larger quantity of users, 24/7 permissions management, 24/7 monitoring, all without requiring physical proximity to the app.","PeriodicalId":429684,"journal":{"name":"Proceedings of the 6th International Conference on Robotics and Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132997721","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
Face Recognition from Depth Images with Convolutional Neural Network 基于卷积神经网络的深度图像人脸识别
Proceedings of the 6th International Conference on Robotics and Artificial Intelligence Pub Date : 2020-11-20 DOI: 10.1145/3449301.3449305
Huiping Wang, Yang Tan, Xiu-qing Liu, Nian Liu, Boyu Chen
{"title":"Face Recognition from Depth Images with Convolutional Neural Network","authors":"Huiping Wang, Yang Tan, Xiu-qing Liu, Nian Liu, Boyu Chen","doi":"10.1145/3449301.3449305","DOIUrl":"https://doi.org/10.1145/3449301.3449305","url":null,"abstract":"In recent years, the rapid development of face recognition technology has made it a hot research field. Depth image has been widely studied in face recognition due to its advantages of three-dimensional information and light insensitivity. The traditional depth image recognition method mainly focuses on the design of manual features, and it is often difficult to achieve an ideal recognition effect. This paper proposes a Convolutional Neural Network (CNN) structure for face recognition in depth images. And experiments on the RGB-D-T face database show that the proposed CNN structure can significantly improve the face recognition accuracy, compared with traditional face recognition methods, such as LBP, moment invariant and PCA.","PeriodicalId":429684,"journal":{"name":"Proceedings of the 6th International Conference on Robotics and Artificial Intelligence","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133460945","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
TV Series Ratings Analysis and Prediction Based on Decision Tree 基于决策树的电视剧收视率分析与预测
Proceedings of the 6th International Conference on Robotics and Artificial Intelligence Pub Date : 2020-11-20 DOI: 10.1145/3449301.3449326
Rui Hu
{"title":"TV Series Ratings Analysis and Prediction Based on Decision Tree","authors":"Rui Hu","doi":"10.1145/3449301.3449326","DOIUrl":"https://doi.org/10.1145/3449301.3449326","url":null,"abstract":"In the new era of the rapid development of the film and television industry, audience rating, as an important indicator for evaluating film and television works, and an important reference for program production, arrangement, adjustment, plays a significant role in the film and television industry. Therefore, it is necessary to predict the audience rating of TV series to assist the production and arrangement of TV series. This paper selects relevant information about popular TV series in 2019 to analyze the influences of six factors, including broadcast time period, score on Douban.com, main actors, directors, and broadcasting platform, on TV series ratings through two different decision tree models. On this basis, this paper compares the experimental results of the two models through many experiments, and chooses ID3 decision tree algorithm as the prediction model of TV series ratings. The results show that the prediction model constructed in this paper has a good effect, and the accuracy rate can reach 84.05%, which can be used to predict TV series audience rating.","PeriodicalId":429684,"journal":{"name":"Proceedings of the 6th International Conference on Robotics and Artificial Intelligence","volume":"40 5, Part 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130484544","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
A Two-Stream Network for Underwater Acoustic Target Classification 水声目标分类的两流网络
Proceedings of the 6th International Conference on Robotics and Artificial Intelligence Pub Date : 2020-11-20 DOI: 10.1145/3449301.3449343
Guanghui Xing, Peishun Liu, Hui Zhang, Ruichun Tang, Yaguang Yin
{"title":"A Two-Stream Network for Underwater Acoustic Target Classification","authors":"Guanghui Xing, Peishun Liu, Hui Zhang, Ruichun Tang, Yaguang Yin","doi":"10.1145/3449301.3449343","DOIUrl":"https://doi.org/10.1145/3449301.3449343","url":null,"abstract":"Due to the complex marine environment, underwater acoustic signals may lose some feature information in the process of transmission, resulting in limited classification accuracy. In order to achieve higher classification accuracy, we propose a novel network structure to deal with different features of underwater acoustic signals. In the processing of underwater acoustic signal, the accuracy of Visual Geometry Group (VGG) is 2.3% higher than ResNet50, and the accuracy of Gate Recurrent Unit (GRU) is 1.1% higher than Long Short-Term Memory(LSTM) and 4.2% higher than Recurrent neural network (RNN). The proposed method consists of two parts: (1) MFCCNet: A GRU based network for training features from MFCC. (2) SpecNet: Using VGG to process features from spectrogram. The two parts are connected by a fully connected layer for the final output. The integration of SpecNet and MFCCNet promotes the whole network to learn deeper features. Experiments show that our method achieves 98.8% accuracy in the actual data set of civil ships.","PeriodicalId":429684,"journal":{"name":"Proceedings of the 6th International Conference on Robotics and Artificial Intelligence","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123249039","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
Multi-focus Image Fusion Based on Multiple CNNs in NSCT Domain 基于NSCT域多个cnn的多焦点图像融合
Proceedings of the 6th International Conference on Robotics and Artificial Intelligence Pub Date : 2020-11-20 DOI: 10.1145/3449301.3449314
Wenqing Wang, Xiaoyu Wang, Xiao Ma, Han Liu
{"title":"Multi-focus Image Fusion Based on Multiple CNNs in NSCT Domain","authors":"Wenqing Wang, Xiaoyu Wang, Xiao Ma, Han Liu","doi":"10.1145/3449301.3449314","DOIUrl":"https://doi.org/10.1145/3449301.3449314","url":null,"abstract":"In order to overcome the boundary information loss in the image fusion with single convolutional neural network, this paper proposes a novel multi-focus image fusion with multiple convolutional neural networks in nonsubsampled contourlet transform (NSCT) domain. First, the source images are decomposed into a low frequency sub-band and a serious of high frequency sub-bands by using NSCT. Second, a corresponding CNN model for each level of high frequency sub-bands is trained to fuse them. Then, an averaging rule is employed to fuse the low frequency sub-bands. Finally, the fused image is reconstructed by performing inverse NSCT on the fused sub-bands. Experimental results illustrate that the proposed method is superior to several existing multi-focus image fusion methods in terms of both executive evaluation and objective evaluation.","PeriodicalId":429684,"journal":{"name":"Proceedings of the 6th International Conference on Robotics and Artificial Intelligence","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115029049","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
Deep Learning Based Low-light Enhancement and Noise Suppression in USV Imaging System 基于深度学习的USV成像系统弱光增强和噪声抑制
Proceedings of the 6th International Conference on Robotics and Artificial Intelligence Pub Date : 2020-11-20 DOI: 10.1145/3449301.3449318
Peizheng Li, Caofei Luo, F. Wu, Jianan Zheng, Sainan Ma
{"title":"Deep Learning Based Low-light Enhancement and Noise Suppression in USV Imaging System","authors":"Peizheng Li, Caofei Luo, F. Wu, Jianan Zheng, Sainan Ma","doi":"10.1145/3449301.3449318","DOIUrl":"https://doi.org/10.1145/3449301.3449318","url":null,"abstract":"With the rapid development of artificial intelligence technology and autonomous navigation technology, the unmanned surface vessel (USV) industry has developed accordingly, and it has played an important role in the fields of water quality monitoring, maritime inspection, and maritime safety assurance. However, USV is easily affected by the external lighting environment. In the case of insufficient lighting, the collected images have the characteristics of low brightness, low contrast and low resolution, and are extremely susceptible to external noise interference, making USV difficult obtain input requirements that meet the visual tasks such as target recognition and semantic segmentation. In this paper, we propose a deep learning-based low-light image enhancement and noise suppression method (LENet). Specifically, LENet is used to map the low-light image to the normal-light image through a deep Unet network, and CBM3D further suppresses the interference noise in the image to achieve the enhancement of the low-light image. We enhance the generalization ability and robustness of the deep network by embedding dilated convolutions and dense blocks in the deep Unet network. Structural similarity (SSIM) and norm are used as the loss function to further improve the quality of the enhanced image. The experimental results show that the deep network proposed in this paper improves the brightness and contrast of the images collected by the USV under insufficient lighting conditions, which can meet the input requirements of the USV visual task.","PeriodicalId":429684,"journal":{"name":"Proceedings of the 6th International Conference on Robotics and Artificial Intelligence","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121642908","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
Tensor Voting Based 3-D Point Cloud Processing for Downsampling and Registration 基于张量投票的三维点云降采样配准处理
Proceedings of the 6th International Conference on Robotics and Artificial Intelligence Pub Date : 2020-11-20 DOI: 10.1145/3449301.3449312
Osman Ervan, H. Temeltas
{"title":"Tensor Voting Based 3-D Point Cloud Processing for Downsampling and Registration","authors":"Osman Ervan, H. Temeltas","doi":"10.1145/3449301.3449312","DOIUrl":"https://doi.org/10.1145/3449301.3449312","url":null,"abstract":"Point cloud registration is related with many significant and compelling 3D perception problems including simultaneous localization and mapping (SLAM), 3D object reconstruction, dense 3D environment generation, pose estimation, and object tracking. A point cloud can be defined as a data format that consists of a combination of multiple points used to identify an object or environment. The aim of this study is to propose a point cloud registration method, which ensures that the point clouds obtained with 3D LiDAR are sampled while preserving their geometric features and the point clouds are registered with high success rate. For this process, it is inspired from the method known in the literature as Tensor Voting, which is originally used to extract geometric features in N-dimensional space. In point cloud registration process, a coarse registration step has been proposed, which focusses on feature registration instead of point registration.","PeriodicalId":429684,"journal":{"name":"Proceedings of the 6th International Conference on Robotics and Artificial Intelligence","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125295485","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
p-A Multi-metric Decision Theory Based Adaptive Routing for Mobile Sparse Underwater Acoustic Sensor Network 基于p-A多度量决策理论的移动稀疏水声传感器网络自适应路由
Proceedings of the 6th International Conference on Robotics and Artificial Intelligence Pub Date : 2020-11-20 DOI: 10.1145/3449301.3449340
Xiaofang Deng, Liuyue Shi, Liang Huang, Liyan Luo, H. Qiu
{"title":"p-A Multi-metric Decision Theory Based Adaptive Routing for Mobile Sparse Underwater Acoustic Sensor Network","authors":"Xiaofang Deng, Liuyue Shi, Liang Huang, Liyan Luo, H. Qiu","doi":"10.1145/3449301.3449340","DOIUrl":"https://doi.org/10.1145/3449301.3449340","url":null,"abstract":"Mobile and sparse underwater acoustic sensor networks (MS-UASNs) have attracted much attention due to their wide applications in various fields. However, void holes caused by the movement of nodes and the sparse deployment of networks, pose many challenges to design reliable routing protocol for MS-UASNs. Thereby, selecting the next-hop forwarder merely according to the state of current node may lead to the failure of forwarding in the local sparse region. To deal with the problem of void holes in sparse networks, in this paper we propose a Multi-metric decision theory based adaptive routing protocol (MDARP). The novelty of MDARP is that the selection of relay candidate nodes considers not only the depth of expected next hop, but also the continuable degree (CD) of all subsequent hops. By this method, the probability of encountering voids is reduced effectively. Meanwhile, the source node evaluates the quality of candidates by taking into account the CD, stable degree (SD) and energy cost (EC) based on Multi-metric decision theory (M2DT), and then the optimal next hop is determined. Subsequently, the source node enables to schedule the packets transmission toward the destination efficiently based on the quality of nodes. The simulation results represent that the MDARP protocol shows better performance in terms of packet delivery ratio, energy efficiency and end-to-end delay in MS-UASNs.","PeriodicalId":429684,"journal":{"name":"Proceedings of the 6th International Conference on Robotics and Artificial Intelligence","volume":"519 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123116899","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
Underwater Acoustic Target Recognition Based on ReLU Gated Recurrent Unit 基于ReLU门控循环单元的水声目标识别
Proceedings of the 6th International Conference on Robotics and Artificial Intelligence Pub Date : 2020-11-20 DOI: 10.1145/3449301.3449309
Xiaodong Sun, Xiaohan Yin, Yaguang Yin, Peishun Liu, Liang Wang, Ruichun Tang
{"title":"Underwater Acoustic Target Recognition Based on ReLU Gated Recurrent Unit","authors":"Xiaodong Sun, Xiaohan Yin, Yaguang Yin, Peishun Liu, Liang Wang, Ruichun Tang","doi":"10.1145/3449301.3449309","DOIUrl":"https://doi.org/10.1145/3449301.3449309","url":null,"abstract":"In general, the traditional acoustic models'(e. g. Gaussian mixture model, GMM) for underwater acoustic target recognition (UATR) performance in sequential data has so far been disappointing. In contrast, Recurrent Neural Network (RNN) is a powerful tool for sequential data. This paper investigates the Gated Recurrent Unit (GRU), which is a variant of the RNN. We use the Rectified Linear Unit (ReLU) activation, and carry out experiments on the real acoustic dataset of ships. The recognition accuracy on the dataset with noise reach 86.1%, on dataset without noise reach 96.4%. Both performances are better than other baselines.","PeriodicalId":429684,"journal":{"name":"Proceedings of the 6th International Conference on Robotics and Artificial Intelligence","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115876226","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
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