Pub Date : 2020-07-01DOI: 10.1109/ICIVC50857.2020.9177444
Shuning Li, Huasheng Zhu, Kaiwen Zha, Wei Li
In the realistic video monitoring environment, the traditional super-resolution reconstruction technique based on prior knowledge is not suitable for monitoring the super-resolution reconstruction of the image. In this paper, a super-resolution reconstruction algorithm of target image based on learning background is proposed. The first part of the algorithm is to design a non-manifolds consistency algorithm for super-resolution reconstruction of the whole video surveillance image. The second part of the algorithm, from video surveillance images in the background, to select the characteristics significantly, and the relatively fixed background. And then to study the background, study a mapping function can improve image quality. Finally, the mapping function to restoration image of interested target, so that we can better recover the structure and texture of target image details. The experimental results show that the proposed algorithm improves both the objective evaluation index and the subjective visual effect.
{"title":"Super-Resolution Reconstruction Algorithm of Target Image Based on Learning Background","authors":"Shuning Li, Huasheng Zhu, Kaiwen Zha, Wei Li","doi":"10.1109/ICIVC50857.2020.9177444","DOIUrl":"https://doi.org/10.1109/ICIVC50857.2020.9177444","url":null,"abstract":"In the realistic video monitoring environment, the traditional super-resolution reconstruction technique based on prior knowledge is not suitable for monitoring the super-resolution reconstruction of the image. In this paper, a super-resolution reconstruction algorithm of target image based on learning background is proposed. The first part of the algorithm is to design a non-manifolds consistency algorithm for super-resolution reconstruction of the whole video surveillance image. The second part of the algorithm, from video surveillance images in the background, to select the characteristics significantly, and the relatively fixed background. And then to study the background, study a mapping function can improve image quality. Finally, the mapping function to restoration image of interested target, so that we can better recover the structure and texture of target image details. The experimental results show that the proposed algorithm improves both the objective evaluation index and the subjective visual effect.","PeriodicalId":6806,"journal":{"name":"2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC)","volume":"37 1","pages":"133-138"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73526336","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}
Pub Date : 2020-07-01DOI: 10.1109/ICIVC50857.2020.9177475
Shan Li, Huizhi Xu, Zijun Liang, Chengyuan Mao
In order to improve the traffic efficiency of public transportation, alleviate the urban traffic congestion and improve the overall traffic efficiency. On the one hand, this paper proposed a method for identifying bus vehicles with signal priority based on video detectors, which solved the shortcomings of current GPS signal priority control methods such as inaccurate GPS positioning, unstable wireless communication transmission signals, and high construction costs. On the other hand, the algorithm logic of bus signal priority control was proposed. The algorithm took average passenger delay as the optimization index, realizes the signal priority of bus at urban intersections. At the same time, considered reducing the negative impact of public transport signal priority on non-priority social vehicles. Finally, the simulation test of actual urban intersection cases was carried out by using VISSIM micro simulation software. The simulation results showed that, compared with the traditional signal control method, considered the bus signal priority signal timing scheme can effectively reduce the average passenger delay and vehicle queue length, and further improve the traffic efficiency of the intersection.
{"title":"Bus Signal Priority Control Method Based on Video Detection Technology at Urban Intersection","authors":"Shan Li, Huizhi Xu, Zijun Liang, Chengyuan Mao","doi":"10.1109/ICIVC50857.2020.9177475","DOIUrl":"https://doi.org/10.1109/ICIVC50857.2020.9177475","url":null,"abstract":"In order to improve the traffic efficiency of public transportation, alleviate the urban traffic congestion and improve the overall traffic efficiency. On the one hand, this paper proposed a method for identifying bus vehicles with signal priority based on video detectors, which solved the shortcomings of current GPS signal priority control methods such as inaccurate GPS positioning, unstable wireless communication transmission signals, and high construction costs. On the other hand, the algorithm logic of bus signal priority control was proposed. The algorithm took average passenger delay as the optimization index, realizes the signal priority of bus at urban intersections. At the same time, considered reducing the negative impact of public transport signal priority on non-priority social vehicles. Finally, the simulation test of actual urban intersection cases was carried out by using VISSIM micro simulation software. The simulation results showed that, compared with the traditional signal control method, considered the bus signal priority signal timing scheme can effectively reduce the average passenger delay and vehicle queue length, and further improve the traffic efficiency of the intersection.","PeriodicalId":6806,"journal":{"name":"2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC)","volume":"114 1","pages":"256-260"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88687652","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}
Pub Date : 2020-07-01DOI: 10.1109/ICIVC50857.2020.9177434
Leyuan Liu, Jian He, Yibin Hou, Cheng Zhang
The bus passenger data are very important for urban bus dispatching management. When passengers get on or off the bus, they often hide from each other. It is a great challenge for automatically accounting passengers through camera. The traditionally video-based target detection algorithm or target tracking algorithm is difficult to accurately count the number of passenger on and off. In this paper, the YOLOv2 algorithm is combined with the MIL tracker so as to real-time account the number of passengers in the bus surveillance video. Experiment shows that the accuracy rate of bus passenger statistics proposed in this paper reaches over 99%, and it proves that our method has good real-time and high accuracy.
{"title":"A Technology for Automatically Counting Bus Passenger Based on YOLOv2 and MIL Algorithm","authors":"Leyuan Liu, Jian He, Yibin Hou, Cheng Zhang","doi":"10.1109/ICIVC50857.2020.9177434","DOIUrl":"https://doi.org/10.1109/ICIVC50857.2020.9177434","url":null,"abstract":"The bus passenger data are very important for urban bus dispatching management. When passengers get on or off the bus, they often hide from each other. It is a great challenge for automatically accounting passengers through camera. The traditionally video-based target detection algorithm or target tracking algorithm is difficult to accurately count the number of passenger on and off. In this paper, the YOLOv2 algorithm is combined with the MIL tracker so as to real-time account the number of passengers in the bus surveillance video. Experiment shows that the accuracy rate of bus passenger statistics proposed in this paper reaches over 99%, and it proves that our method has good real-time and high accuracy.","PeriodicalId":6806,"journal":{"name":"2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC)","volume":"39 1","pages":"166-170"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82604749","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}
Pub Date : 2020-07-01DOI: 10.1109/ICIVC50857.2020.9177454
Xue Ke, Wei Lin, Gaojie Chen, Quan Chen, Xianzhi Qi, Jie Ma
Low-light image enhancement technology has been developed in recent years. However, most existing related methods need to adjust too many arguments or performs unstably when the environment differs greatly. In our paper, we propose a novel low-light image enhancement method named enhanced deep convolutional low-light image enhancement network (EDLLIE-Net) to address these problems. Firstly, our proposed method extracts multi-scale feature map, which can improve the utilization of context information. Subsequently, our proposed method rescales the feature map by attention mechanism to perceive the most useful information and characteristics. Finally, our proposed method uses encode-decode and residual-learning architecture to obtain the normal image from low-light image. To prove the effectiveness of our proposed model, we evaluate it from two aspects. On one hand, we show EDLLIE-Net can not only handle different dark scenes effectively but also achieve better performance than other representative methods by common metric judgement. On the other hand, a novel evaluation method by combining enhanced result and high-level vision task is proposed, we show our proposed method can gain the higher improvement degree for high-level vision tasks.
{"title":"EDLLIE-Net: Enhanced Deep Convolutional Networks for Low-Light Image Enhancement","authors":"Xue Ke, Wei Lin, Gaojie Chen, Quan Chen, Xianzhi Qi, Jie Ma","doi":"10.1109/ICIVC50857.2020.9177454","DOIUrl":"https://doi.org/10.1109/ICIVC50857.2020.9177454","url":null,"abstract":"Low-light image enhancement technology has been developed in recent years. However, most existing related methods need to adjust too many arguments or performs unstably when the environment differs greatly. In our paper, we propose a novel low-light image enhancement method named enhanced deep convolutional low-light image enhancement network (EDLLIE-Net) to address these problems. Firstly, our proposed method extracts multi-scale feature map, which can improve the utilization of context information. Subsequently, our proposed method rescales the feature map by attention mechanism to perceive the most useful information and characteristics. Finally, our proposed method uses encode-decode and residual-learning architecture to obtain the normal image from low-light image. To prove the effectiveness of our proposed model, we evaluate it from two aspects. On one hand, we show EDLLIE-Net can not only handle different dark scenes effectively but also achieve better performance than other representative methods by common metric judgement. On the other hand, a novel evaluation method by combining enhanced result and high-level vision task is proposed, we show our proposed method can gain the higher improvement degree for high-level vision tasks.","PeriodicalId":6806,"journal":{"name":"2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC)","volume":"111 1","pages":"59-68"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83557179","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}
Pub Date : 2020-07-01DOI: 10.1109/ICIVC50857.2020.9177485
Xian Li, Haiyan Wang, Junkai Shao
This article discusses the influence of icon shape complexity and composition on icon search in military geographical intelligence system. We designed an experiment to explore the influence of icon shape complexity and composition on icon search efficiency in military geographic intelligence system (GIS). The reaction time and accuracy are used in the experiment to reflect people's search efficiency under different experimental conditions. The experimental results provide a scientific reference for the design of military GIS interface by analyzing the effect of different icon shapes and icon presentation on the visual search performance of military GIS interface. Through experiments, it is found that when the icon shape complexity level is the highest or the lowest, it shows poor search performance; when the complexity level is H3, it shows the best search performance; the icon composition does not affect the search performance of the icon on the map background. Significant, but when the complexity level is H2 and H4, there is interaction between different icon composition. This design will provide a reference for the design of GIS interface icons. The experimental results provide a scientific reference for the design of military GIS interface.
{"title":"Research on the Influence of Icon Shape Complexity and Composition on Visual Search Based on Military Geographic Intelligence System","authors":"Xian Li, Haiyan Wang, Junkai Shao","doi":"10.1109/ICIVC50857.2020.9177485","DOIUrl":"https://doi.org/10.1109/ICIVC50857.2020.9177485","url":null,"abstract":"This article discusses the influence of icon shape complexity and composition on icon search in military geographical intelligence system. We designed an experiment to explore the influence of icon shape complexity and composition on icon search efficiency in military geographic intelligence system (GIS). The reaction time and accuracy are used in the experiment to reflect people's search efficiency under different experimental conditions. The experimental results provide a scientific reference for the design of military GIS interface by analyzing the effect of different icon shapes and icon presentation on the visual search performance of military GIS interface. Through experiments, it is found that when the icon shape complexity level is the highest or the lowest, it shows poor search performance; when the complexity level is H3, it shows the best search performance; the icon composition does not affect the search performance of the icon on the map background. Significant, but when the complexity level is H2 and H4, there is interaction between different icon composition. This design will provide a reference for the design of GIS interface icons. The experimental results provide a scientific reference for the design of military GIS interface.","PeriodicalId":6806,"journal":{"name":"2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC)","volume":"1 1","pages":"247-250"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89560672","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}
Pub Date : 2020-07-01DOI: 10.1109/ICIVC50857.2020.9177489
Anestis Staikopoulos, V. Kanakaris, G. Papakostas
Long Range (Lora) technology is assumed to be the subsequent of the wireless communication for the Internet of Things (IoT). Although LoRa provides emulative characteristics, such as a wider cover range, lower expenditure and decreased energy consumption, the usable narrow bandwidth for physical layer modulation in LoRa makes it inapplicable for high bit rate data transmission from devices like visual sensors. Nevertheless, because data from images are larger than those that origin from sensors, one of the main problems of visual IoT is the efficiency of image transmission in networks where bandwidth is narrow. This paper, aims at reviewing the available methods applied to transfer images via LoRa infrastructure, for the first time in the literature. The limitations of each method are pointed out and the challenges that need to be handled in the future are also defined towards establishing a reliable image transfer over a LoRa network.
{"title":"Image Transmission via LoRa Networks – A Survey","authors":"Anestis Staikopoulos, V. Kanakaris, G. Papakostas","doi":"10.1109/ICIVC50857.2020.9177489","DOIUrl":"https://doi.org/10.1109/ICIVC50857.2020.9177489","url":null,"abstract":"Long Range (Lora) technology is assumed to be the subsequent of the wireless communication for the Internet of Things (IoT). Although LoRa provides emulative characteristics, such as a wider cover range, lower expenditure and decreased energy consumption, the usable narrow bandwidth for physical layer modulation in LoRa makes it inapplicable for high bit rate data transmission from devices like visual sensors. Nevertheless, because data from images are larger than those that origin from sensors, one of the main problems of visual IoT is the efficiency of image transmission in networks where bandwidth is narrow. This paper, aims at reviewing the available methods applied to transfer images via LoRa infrastructure, for the first time in the literature. The limitations of each method are pointed out and the challenges that need to be handled in the future are also defined towards establishing a reliable image transfer over a LoRa network.","PeriodicalId":6806,"journal":{"name":"2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC)","volume":"80 1","pages":"150-154"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91548353","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}
Pub Date : 2020-07-01DOI: 10.1109/ICIVC50857.2020.9177447
Xiang Wang, Ke-bin Jia
With the increase of the elderly population, the phenomenon of the elderly falling at home or out is more and more common. Therefore, fall detection is of great significance for the health protection of the elderly. Throughout the research of fall detection at home and abroad, most of the fall detection based on video monitoring is complex and redundant, which affects the real-time and accuracy of detection. In view of the above problems, this paper proposes a fall detection method based on video in complex environment, aiming to detect fall behavior more accurately and quickly. The main work of this paper is as follows: firstly, YOLOv3 network model is proposed for detection algorithm. Secondly, the human fall detection data set is constructed by referring to Pascal VOC data set format. Then, the algorithm model is optimized and trained in GPU (graphic processing unit) deep learning server. Finally, comparison of test results with our YOLOv3 network model and other detection algorithms shows that our detection algorithm has a good recognition effect.
{"title":"Human Fall Detection Algorithm Based on YOLOv3","authors":"Xiang Wang, Ke-bin Jia","doi":"10.1109/ICIVC50857.2020.9177447","DOIUrl":"https://doi.org/10.1109/ICIVC50857.2020.9177447","url":null,"abstract":"With the increase of the elderly population, the phenomenon of the elderly falling at home or out is more and more common. Therefore, fall detection is of great significance for the health protection of the elderly. Throughout the research of fall detection at home and abroad, most of the fall detection based on video monitoring is complex and redundant, which affects the real-time and accuracy of detection. In view of the above problems, this paper proposes a fall detection method based on video in complex environment, aiming to detect fall behavior more accurately and quickly. The main work of this paper is as follows: firstly, YOLOv3 network model is proposed for detection algorithm. Secondly, the human fall detection data set is constructed by referring to Pascal VOC data set format. Then, the algorithm model is optimized and trained in GPU (graphic processing unit) deep learning server. Finally, comparison of test results with our YOLOv3 network model and other detection algorithms shows that our detection algorithm has a good recognition effect.","PeriodicalId":6806,"journal":{"name":"2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC)","volume":"22 1","pages":"50-54"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79293851","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}
Pub Date : 2020-07-01DOI: 10.1109/ICIVC50857.2020.9177476
Yuhang Jiang, Yuwei Lu, Lili Dong, Wenhai Xu
In this paper, a novel multi-frame image super-resolution algorithm for small amount of data is proposed. Our method solve the problem that the spatial resolution of the reconstructed image is low and the visual quality of it is poor when the number of input low-resolution images is small. In order to improve the quality of the initial estimation, we construct the initial estimation with multi-frame low-resolution images according to the registration parameter and interpolate the missing pixels by directional Gaussian-like filtering. In order to solve the problem of fuzzy initial estimation, the enhancement method is used to highlight the image details. A large number of qualitative and quantitative evaluation results show that our method has strong reconstruction performance for various types of low-resolution images under different amount of data.
{"title":"Multi-frame Image Super-Resolution Algorithm Based on Small Amount of Data","authors":"Yuhang Jiang, Yuwei Lu, Lili Dong, Wenhai Xu","doi":"10.1109/ICIVC50857.2020.9177476","DOIUrl":"https://doi.org/10.1109/ICIVC50857.2020.9177476","url":null,"abstract":"In this paper, a novel multi-frame image super-resolution algorithm for small amount of data is proposed. Our method solve the problem that the spatial resolution of the reconstructed image is low and the visual quality of it is poor when the number of input low-resolution images is small. In order to improve the quality of the initial estimation, we construct the initial estimation with multi-frame low-resolution images according to the registration parameter and interpolate the missing pixels by directional Gaussian-like filtering. In order to solve the problem of fuzzy initial estimation, the enhancement method is used to highlight the image details. A large number of qualitative and quantitative evaluation results show that our method has strong reconstruction performance for various types of low-resolution images under different amount of data.","PeriodicalId":6806,"journal":{"name":"2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC)","volume":"67 1","pages":"118-122"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76289560","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}
Pub Date : 2020-07-01DOI: 10.1109/ICIVC50857.2020.9177437
Miao Cheng, Jinpeng Su, Luyi Li, Xiangming Zhou
In order to weak the variation of the object instance caused by the scale, we innovatively propose an object detection detector based on adversarial learning and deformation feature pyramid: A-DFPN. Firstly, in the feature extraction stage, the concept of Deformation Feature Pyramid Module is proposed. The outstanding advantage is that it can fully extract object features from different convolution layers and objects of different scales. In addition, Two Stage Module is also proposed, it gradually perfects the adjusted anchors in the previous stage through multi-step regression, and locates the position and shape of the object in each RPN to make the location more accurate. At the same time, Mask Module increases the robustness of the detector by spatially blocking certain feature maps or by manipulating feature responses to generate difficult samples. Finally, the final bounding boxes is filtered by soft-NMS. Under the Resnet-101 network architecture, our algorithm achieves the mean average precision of 81.1034% on the Pascal VOC 2007 dataset and 73.52% on the DETRAC dataset, reaching the state-of-the-art detection level.
{"title":"A-DFPN: Adversarial Learning and Deformation Feature Pyramid Networks for Object Detection","authors":"Miao Cheng, Jinpeng Su, Luyi Li, Xiangming Zhou","doi":"10.1109/ICIVC50857.2020.9177437","DOIUrl":"https://doi.org/10.1109/ICIVC50857.2020.9177437","url":null,"abstract":"In order to weak the variation of the object instance caused by the scale, we innovatively propose an object detection detector based on adversarial learning and deformation feature pyramid: A-DFPN. Firstly, in the feature extraction stage, the concept of Deformation Feature Pyramid Module is proposed. The outstanding advantage is that it can fully extract object features from different convolution layers and objects of different scales. In addition, Two Stage Module is also proposed, it gradually perfects the adjusted anchors in the previous stage through multi-step regression, and locates the position and shape of the object in each RPN to make the location more accurate. At the same time, Mask Module increases the robustness of the detector by spatially blocking certain feature maps or by manipulating feature responses to generate difficult samples. Finally, the final bounding boxes is filtered by soft-NMS. Under the Resnet-101 network architecture, our algorithm achieves the mean average precision of 81.1034% on the Pascal VOC 2007 dataset and 73.52% on the DETRAC dataset, reaching the state-of-the-art detection level.","PeriodicalId":6806,"journal":{"name":"2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC)","volume":"40 1","pages":"11-18"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77176592","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}
Pub Date : 2020-07-01DOI: 10.1109/ICIVC50857.2020.9177436
Yao Wang, Yi Zhang, Jiamei Chen, Yang Long, Yang Yang
Cognitive radio (CR) is proposed as a critical means to reuse the primary spectrum in recent years. However, the cognitive node mobility has not fully researched for the mobile cognitive radio networks (CRNs). In this paper, a support vector machine (SVM) based spectrum assignment scheme is presented in the Manhattan city mobility environments, which takes the position and speed information of cognitive nodes into consideration during the spectrum availability prediction. Numerical results show good performance in the total spectrum utilization comparing with the traditional resource allocation algorithms.
{"title":"Support Vector Machine Based Spectrum Allocation Scheme for the Mobile Cognitive Radio Manhattan City Environments","authors":"Yao Wang, Yi Zhang, Jiamei Chen, Yang Long, Yang Yang","doi":"10.1109/ICIVC50857.2020.9177436","DOIUrl":"https://doi.org/10.1109/ICIVC50857.2020.9177436","url":null,"abstract":"Cognitive radio (CR) is proposed as a critical means to reuse the primary spectrum in recent years. However, the cognitive node mobility has not fully researched for the mobile cognitive radio networks (CRNs). In this paper, a support vector machine (SVM) based spectrum assignment scheme is presented in the Manhattan city mobility environments, which takes the position and speed information of cognitive nodes into consideration during the spectrum availability prediction. Numerical results show good performance in the total spectrum utilization comparing with the traditional resource allocation algorithms.","PeriodicalId":6806,"journal":{"name":"2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC)","volume":"18 1","pages":"283-286"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90293414","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}
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