Review on Kernel based Target Tracking for Autonomous Driving

Wang, Yanming; Yue, Jiguang; Dong, Yaomin; Hu, Zhencheng

doi:10.2197/ipsjjip.24.49

Cited by 18 publications

(14 citation statements)

References 107 publications

(115 reference statements)

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“…The speed signal control of high-speed trains needs to consider many factors, such as line speed, train speed limit, ramps, curves, and target points. There have been many studies on the speed control of high-speed trains, but in practical applications, advanced control algorithms and control methods are still relatively few, and the control algorithm is mainly based on classic PID control [23]. Therefore, in this paper, a specific multimodal control method is designed in combination with the operation goals of highspeed train safety, efficiency, and comfort.…”

Section: Fusion Modelmentioning

confidence: 99%

[Retracted] Multimode Intelligent Control Based on Multidata Fusion Filtering in High‐Speed Train Traffic Signal and Control

Huang

2021

Journal of Sensors

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As the speed of high-speed trains continues to increase, the intelligent monitoring of high-speed trains has become a concern of people. This research mainly discusses the application of multimode intelligent control of multidata fusion filtering in high-speed train traffic signal and control. In multimodal intelligent control, BangBang, PI control, adaptive fuzzy PID control, and expert monitoring control under special circumstances can be used, respectively, according to the error and the rate of change of the error, which can achieve the best control effect under safe conditions. Take the allowable speed of ATP as the target speed of the control system, and combine the operation process, operation requirements, traction characteristics, braking characteristics of high-speed trains, and meet the two conditions for improving the operating efficiency of high-speed trains. According to the dynamic expected speed value of high-speed trains, dynamically adjust the switching threshold. This study uses a pulse signal generator to simulate the speed data of the vehicle speed sensor (all pulse data), and then read the speed (pulse) signal data through the pulse signal acquisition card, and display the simulated speed data under the Kingview software. The monitoring computer is used to collect train speed information, display speed information, manage speed information, and output speed information. Then, through OPC technology, the simulation speed data is transmitted to MATLAB software for multidata fusion filtering processing and multimodal control simulation. In the simulation process, the train adopts a multimodal intelligent control response scheme, with a total time of 2183.7 s, which is shortened by 214.5 s and improved by nearly 10%. The multimode intelligent control scheme of multidata fusion filtering proposed in this study can better meet the control of high-speed train traffic signals.

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Section: Fusion Modelmentioning

confidence: 99%

[Retracted] Multimode Intelligent Control Based on Multidata Fusion Filtering in High‐Speed Train Traffic Signal and Control

Huang

2021

Journal of Sensors

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“…Based on the problems in the application of the Internet of ings identification technology in the smart library, the improvement measures for the construction of the smart library are proposed [20,21]. e three relationships between wearable technology and smart library, the specific application of wearable technology in reader navigation, and help for disadvantaged groups and personalized services are analyzed [22,23]. Using the effect of virtual reality to present pictures, models, or videos in the book, the reading method and interest are increased [24,25].…”

Section: Introductionmentioning

confidence: 99%

Visualization of Information Retrieval in Smart Library Based on Virtual Reality Technology

Fang

2020

Complexity

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Starting from the virtual reality technology, the characteristics of its most suitable combination with the library are explored, so as to lay a foundation in theory and practice to promote the development of virtual reality in the library. In the concentration camp of the latest advanced technology, the relevant technologies used in the various levels of models in the smart library are extracted, and their functional principles and applications are systematically introduced; Chapter 4 builds the level of the smart library information retrieval technology model. The structure diagram, various levels of functions, and related smart service models are discussed. Research is on context construction of extended resources of knowledge service in smart libraries. The elements and content of the resource context are introduced, and the strategy of constructing the resource context for the extension of the knowledge service of the smart library is proposed. Research is on the construction of the context of the extension and interconnection of knowledge services in smart libraries. The relevant elements and contents of the technical context and the spatial context are introduced, and a strategy for constructing a connected context for the extension of the knowledge service of a smart library is proposed. Research is on the context construction of knowledge service extension of smart library. The elements and contents of the service context are introduced, and the strategy of constructing the service context of the knowledge service extension of the smart library is proposed. A visual model of information retrieval is constructed. The model integrates the core steps of the information visualization process and introduces the information space and functional system (navigation, organization, indexing, and retrieval) in the information construction theory into it and through five mapping layers (functional space mapping, visualization space mapping, visualization mapping, view mapping, and interactive mapping) and six spatial layers (role space, information space, functional space, visualized information space, visualized object space, and visualized view space), which describe visualization applications that target the user experience model in information construction build process.

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“…e functions of the system for film and television include automatic segmentation of shots, key frame extraction of shots, static sample query, and text and title content query [3,4]. It provides a set of tools for people on the Web to search and retrieve images and video, and it can be realized in Internet content-based video/image retrieval [5,6]. Video allows the user to use visual characteristics and to retrieve the relationship between time and space and film and television and has integrated text and visual search method, automatic video object segmentation and tracking, and rich visual feature library, including color, texture, shape, and motion, interactive query, and browsing on the Internet [7,8].…”

Section: Introductionmentioning

confidence: 99%

SIFT Feature‐Based Video Camera Boundary Detection Algorithm

Kong

2021

Complexity

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Aiming at the problem of low accuracy of edge detection of the film and television lens, a new SIFT feature-based camera detection algorithm was proposed. Firstly, multiple frames of images are read in time sequence and converted into grayscale images. The frame image is further divided into blocks, and the average gradient of each block is calculated to construct the film dynamic texture. The correlation of the dynamic texture of adjacent frames and the matching degree of SIFT features of two frames were compared, and the predetection results were obtained according to the matching results. Next, compared with the next frame of the dynamic texture and SIFT feature whose step size is lower than the human eye refresh frequency, the final result is obtained. Through experiments on multiple groups of different types of film and television data, high recall rate and accuracy rate can be obtained. The algorithm in this paper can detect the gradual change lens with the complex structure and obtain high detection accuracy and recall rate. A lens boundary detection algorithm based on fuzzy clustering is realized. The algorithm can detect sudden changes/gradual changes of the lens at the same time without setting a threshold. It can effectively reduce the factors that affect lens detection, such as flash, movies, TV, and advertisements, and can reduce the influence of camera movement on the boundaries of movies and TVs. However, due to the complexity of film and television, there are still some missing and false detections in this algorithm, which need further study.

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Review on Kernel based Target Tracking for Autonomous Driving

Cited by 18 publications

References 107 publications

[Retracted] Multimode Intelligent Control Based on Multidata Fusion Filtering in High‐Speed Train Traffic Signal and Control

[Retracted] Multimode Intelligent Control Based on Multidata Fusion Filtering in High‐Speed Train Traffic Signal and Control

Visualization of Information Retrieval in Smart Library Based on Virtual Reality Technology

SIFT Feature‐Based Video Camera Boundary Detection Algorithm

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