Liu Mingming, Dongbin Pei, Haoxiang Sun, Ju Liu, Zhu Donghui
{"title":"Moving Target Detection Algorithm Combining Three-Frame Difference and Hough","authors":"Liu Mingming, Dongbin Pei, Haoxiang Sun, Ju Liu, Zhu Donghui","doi":"10.2991/ICMEIT-19.2019.11","DOIUrl":"https://doi.org/10.2991/ICMEIT-19.2019.11","url":null,"abstract":"Difference and Hough Mingming Liu1, Dong Pei 1,2, *, Haoxiang Sun 1, Ju Liu 1, Donghui Zhu 1 1College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou Gansu 730030, China; 2Engineering Research Center of Gansu of Gansu Province for Intelligence Information Technology and Application, Lanzhou Gansu 730030, China. *peidong@nwnu.edu.cn Abstract. Aiming at the three-frame difference method, the moving target detection will be misdetected due to the influence of noise. A moving target detection algorithm with improved threeframe difference combined with Hough is proposed. Firstly, the background difference method based on local adaptive threshold is used to reduce the influence of light and noise on moving target detection. Secondly, the differential result of three-frame difference method is introduced to improve the sensitivity of detection. Finally, the two differential results are combined and combined. The Hough transform detects the moving target. The experimental results show that the algorithm can effectively suppress noise and quickly and accurately identify moving targets, which meets the needs of real-time detection.","PeriodicalId":223458,"journal":{"name":"Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125921086","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}
{"title":"Research on Thermocline Tracking based on Multi-AUVs Formation","authors":"Zhen Li, Yiping Li","doi":"10.2991/ICMEIT-19.2019.19","DOIUrl":"https://doi.org/10.2991/ICMEIT-19.2019.19","url":null,"abstract":"Thermocline is of great significance to marine scientific research. Multi-autonomous underwater vehicles (AUVs) have great advantages over single autonomous underwater vehicle in ocean observation. In order to solve the multi-AUVs for Thermocline tracking problem, firstly, an AUV thermocline tracking method is proposed based on Kalman filter estimation algorithm. Then, an AUV motion controller based on state feedback is designed by using H∞ robust control method. Finally, a thermocline tracking method with vertical distribution of multi-AUVs formation is proposed and implemented in simulation environment.","PeriodicalId":223458,"journal":{"name":"Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122477073","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}
{"title":"Design and Implementation of Environmental Detection System based on ZigBee","authors":"Xiangcheng Wu, Wuxing Mao","doi":"10.2991/ICMEIT-19.2019.72","DOIUrl":"https://doi.org/10.2991/ICMEIT-19.2019.72","url":null,"abstract":"With the continuous development of mobile Internet and Internet of Things technology, people's requirements for environmental quality are getting higher and higher. They are eager to use intelligent management systems to detect the environment. In this paper, wireless sensor network with low power consumption based on ZigBee can realize short distance wireless communication. Through distributed sensor nodes, all kinds of states in the environment can be obtained. The coordinator node can obtain the sensor state wirelessly and upload the server side through the serial port. PC and mobile terminals get environmental states with Internet communication. So that they cannot monitor the state of the environment in real time without being restricted by distance. Experimental results show that the system is stable and reliable and it can be monitored remotely in real time.","PeriodicalId":223458,"journal":{"name":"Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)","volume":"31 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131378896","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}
{"title":"Research and Implementation of Multi-scene Image Semantic Segmentation based on Fully Convolutional Neural Network","authors":"F. Yu","doi":"10.2991/ICMEIT-19.2019.27","DOIUrl":"https://doi.org/10.2991/ICMEIT-19.2019.27","url":null,"abstract":"With the rapid development of deep neural networks, image recognition and segmentation are important research issues in computer vision in recent years. This paper proposes an image semantic segmentation method based on Fully Convolutional Networks (FCN), which combines the deconvolution layer and convolutional layer converted from the fully connected layer in the traditional Convolutional Neural Networks (CNN). The multi-scene image data set of the label is model-trained, and the training model is applied to pixel-level segmentation of images containing different targets, and the test results are visualized by writing test modules and the segmentation results of the test set images are colored. The experimental process uses two training modes with different parameters to achieve faster and better convergence, and Mini Batch also are used to adapt to the training of big data sets during training. Finally, through the comparison between the segmentation results of test set and the Ground Truth image, it is proved that the full convolutional neural network training model has a higher validity and Robustness for segmentation of some targets in different scene images.","PeriodicalId":223458,"journal":{"name":"Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133503276","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}
{"title":"Pedestrian Recognition based on Human Semantics and PCA-HOG","authors":"E. Yang, Rong Xie","doi":"10.2991/ICMEIT-19.2019.120","DOIUrl":"https://doi.org/10.2991/ICMEIT-19.2019.120","url":null,"abstract":"In real monitoring scenarios, pedestrian semantics, such as gender and clothing type, is very important for pedestrian retrieval and pedestrian recognition. Traditional pedestrian semantics attribute recognition algorithm adopts manual feature extraction and cannot express the association between pedestrian semantics features. This paper proposes a pedestrian semantics recognition method based on improved AlexNet convolution neural network to obtain pedestrian semantics features. Vector. At the same time, a large number of experiments show that HOG descriptors have a good effect in pedestrian recognition, but the number is too large. In this paper, PCA-HOG descriptors are used to express pedestrians and obtain low-dimensional PCA-HOG eigenvectors. Finally, PCA-HOG feature vectors and pedestrian semantic feature vectors are joined together, and LR model is used to predict pedestrian recognition. Compared with traditional methods, the algorithm is simpler, more practical and has higher recognition accuracy.","PeriodicalId":223458,"journal":{"name":"Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)","volume":"58 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131900452","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}
{"title":"Ping Pong Motion Recognition based on Smart Watch","authors":"Zengjun Fu, Kuang-I Shu, Heng Zhang","doi":"10.2991/ICMEIT-19.2019.99","DOIUrl":"https://doi.org/10.2991/ICMEIT-19.2019.99","url":null,"abstract":"Smart watches have become one of the most representative devices in wearable devices because of their unique advantages such as integration, portability, reliability, stability, universality and low environmental dependence. At present, it is mainly used for the monitoring of health indicators such as human heart rate. Whole-body inertial sensing devices cannot meet the actual needs of the general public for virtual sports because of high prices and inconvenient wear. In this paper, a single piece smart watch is used to study the recognition of the most common actions in table tennis which is a kind of fast-moving sport and has many fans through an improved convolution neural network model. The final experimental results show that the recognition accuracy reaches 95.46%, which can basically meet the needs of amateurs' motionSports.","PeriodicalId":223458,"journal":{"name":"Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134120014","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}
Bing Li, Juan Wang, Ning Li, Minghua Zhao, Wang Wang
{"title":"Application and Development of Database Technology in the Background of Big Data","authors":"Bing Li, Juan Wang, Ning Li, Minghua Zhao, Wang Wang","doi":"10.2991/ICMEIT-19.2019.149","DOIUrl":"https://doi.org/10.2991/ICMEIT-19.2019.149","url":null,"abstract":"Absrtact. The development of big data makes us face many problems when we operate traditional databases and use hundreds of TB or PB data. Many industries such as finance and Telecommunications now involve a lot of data, and a lot of data for the original ones. The database is a great impact. At present, the database technology in the world has made great progress. Many kinds of database technology emerge in endlessly. This paper mainly studies the development status of database technology under the background of large data.","PeriodicalId":223458,"journal":{"name":"Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134554627","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}
{"title":"Unbiased Sampling Method Analysis on Online Social Network","authors":"Siyao Wang, Bo Liu, Jiajun Zhou, Guangpeng Li","doi":"10.2991/ICMEIT-19.2019.39","DOIUrl":"https://doi.org/10.2991/ICMEIT-19.2019.39","url":null,"abstract":"Abstract. The study of social graph structure has become extremely popular with the development of the Online Social Network (OSN). The main bottleneck is that the large account of social data makes it difficult to obtain and analyze, which consume extensive bandwidth, storage and computing resources. Thus unbiased sampling of OSN makes it possible to get accurate and representative properties of OSN graph. The widely used algorithm, Breadth-First Sampling (BFS)and Random Walking (RW) both are proved that there exists substantial bias towards high-degree nodes. By contrast the Metropolis-Hasting random walking (MHRW), re-weighted random walking (RWRW) and the unbiased sampling with reduced self-loop (USRS)which are all based on Markov Chain Monte Carlo(MCMC) method could produce approximate uniform samples. In this paper, we analyze the similarities and differences among the four algorithms, and show the performance of unbiased estimation and crawling efficient on the data set of Facebook. In addition, we provide formal convergence test to determine when the crawling process attain an equilibrium state and the number of nodes should be discarded.","PeriodicalId":223458,"journal":{"name":"Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)","volume":"271 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133822388","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}
{"title":"Feedback-Based Scheduling for Load-Balanced Crosspoint Buffered Crossbar Switches","authors":"Xiangcheng Wu, Wuxing Mao","doi":"10.2991/ICMEIT-19.2019.154","DOIUrl":"https://doi.org/10.2991/ICMEIT-19.2019.154","url":null,"abstract":"With the rapid development of the computer and Internet, users need high-bandwidth and high-stable network environment. Routing and switching, as an important network device, supporting high-quality services. It's always been a focus of researchers. In view of the combined input Crosspoint existing queue (CICQ) controlled by flow control delay, and the pure cross-point buffer (CQ) has the problem of “insufficient throughput performance in unbalanced traffic mode”. There was an algorithm proposes a load-balanced cross-buffer scheduling algorithm, which can improve the performance of throughput, quantity and delay. But the load balanced cell has the problem of out-sequence of the cells in the data stream of the same output port. This paper proposes a feedback-based load-balanced cross-buffer scheduling algorithm to ensure throughput and delay performance. Through feedback scheduling, the problem of out-sequence is well solved.","PeriodicalId":223458,"journal":{"name":"Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)","volume":"293 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114385291","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}
{"title":"Decentralized Location Privacy Protection Method of Offset Grid","authors":"Jie Ling, Junyi Xu","doi":"10.2991/ICMEIT-19.2019.20","DOIUrl":"https://doi.org/10.2991/ICMEIT-19.2019.20","url":null,"abstract":"Location services bring convenience to people's lives, but also easily lead to the disclosure of personal privacy information. Many location privacy protection methods are based on trusted anonymous servers, but in real life, the anonymous servers are not trusted. This paper proposes a decentralized location privacy protection method of offset grid for untrusted anonymous servers. The method divides the location area into grids, and the user calculates the offset grid area according to the periodically updated grid information and sends the offset grid area to the anonymous server. The anonymous server selects K-1 distributed grid coordinates according to the history of other user's query, the anonymous grid points set satisfies the multi-diversity. The method in this paper has lower time overhead while protecting user privacy information, and the location distribution of anonymous location sets is more decentralized. Experiments were carried out on the simulated data set. The experimental results verify the effectiveness of the method.","PeriodicalId":223458,"journal":{"name":"Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114529399","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}