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Vehicle Detection Counting algorithm Based on Background Subtraction algorithm and sort 24

Vehicle Detection Counting Algorithm Based on Background Sub...

24th International Conference on Advanced Communication Technology (ICACT) - Artificial Intelligence Technologies toward Cybersecurity

作者： Guo, Jun Gao, Heyan Yan, Zeyu Cao, Jiahui Fu, Zhenbo Northeastern Univ Software Coll Shenyang Liaoning Peoples R China Northeastern Univ Sch Comp Sci & Engn Shenyang Liaoning Peoples R China

ISBN: (纸本)9791188428090

At present, the deep neural network model is commonly used to detect the vehicle in the video, and the detection accuracy is relatively high. However, the neural network model requires high computing performance and high demand for network transmission bandwidth. In many cases, the edge computing device used is of small computing power, so the neural network model is not applicable. However, background subtraction algorithm is easy to realize because of its low requirement on hardware calculation force and fast and accurate detection speed. Using sort algorithm to track with accurate detection results can improve the speed again and reduce the consumption of computing resources. Therefore, this paper proposes an algorithm that uses the background subtraction algorithm to detect the vehicles in the video, and then uses the sort algorithm to track the detected vehicles. The vehicle counter will automatically count when the vehicle in the video passes the traffic flow counting line. The accuracy of traffic flow counting results in this paper is 88%, which proves the feasibility and effectiveness of vehicle detection counting method based on background subtraction algorithm and sort.

关键词： Background subtraction algorithm sort algorithm Vehicle detection count Edge Computing Image processing

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Vehicle Detection Counting algorithm Based on Background Subtraction algorithm and sort 23

Vehicle Detection Counting Algorithm Based on Background Sub...

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23rd International Conference on Advanced Communications Technology (ICACT) - On-Line security in Pandemic Era

ISBN: (纸本)9791188428069

关键词： Background subtraction algorithm sort algorithm Vehicle detection count Edge Computing Image processing

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German License Plate Recognition System Using the YoloV8 Model and EasyOCR 13

German License Plate Recognition System Using the YoloV8 Mod...

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13th IEEE CPMT Symposium Japan

作者： Aljzaere, Hasan Nath, Atul Chandra Hardt, Wolfram Tech Univ Chemnitz Fac Comp Sci Chemnitz Germany

ISBN: (纸本)9798350377828;9798350377811

License Plate Recognition (LPR) is a highly valuable technology for the modern road-infrastructure, which can accommodate wide-range features. This paper focuses on developing a German License Plate Recognition (LPR) using the technology of the YOLOv8 model and EasyOCR recognition technology. The Python programming language and OpenCV image-processing library are used to develop this system whereas the EasyOCR library is used as an OCR for recognizing characters. The proposed system developed to detect different license plates for a video input and at the same time recognizes both characters and numbers combination for these detected plates. This hybrid mechanism improves performance and accuracy of the detection process by the combination of simultaneous detection and recognition of each license plate for each frame. The over 4000 images of vehicles and German license plates are used as a dataset of this research. By using the YOLOv8 model and EasyOCR recognition model, a license plate detection rate of 95% and a recognition rate of 94% have been achieved on the test results. These results indicate the success of the proposed detection and recognition process on the German Car license Plate.

关键词： German License Plate Recognition Open CV YOLOV8 License Plate detection EasyOCR sort algorithm

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Research and Improvement of PageRank sort algorithm based on Retrieval Results 7

Research and Improvement of PageRank Sort Algorithm based on...

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7th International Conference Intelligent Computation Technology Automation (ICICTA)

作者： Xiang, Lu Zhi Nanning Univ Nanning 530200 Peoples R China

ISBN: (纸本)9781479966363

With the development of Internet, network has become a important platform for information publishment and communications. However, there are tremendous resources in the network, thus it's very difficult for users to find the wanted resources. Search engine can help them find the related information. When users input some keywords, the search engine will return all the web pages related to the keywords in a reasonable order with some sort algorithm. In this process, retrieval results sort algorithm plays a significant role of user satisfaction. This paper starts from the background of sort algorithm, and introduces the history and development of retrieval results sort algorithms. Then three popular sort algorithms are presented, including PageRank algorithm, HillTop algorithm and HITS algoritm. According to the disadvantages of PageRank algorithm, we propose an improved algorithm based on concepts. Lastly this paper discusses the possible applications of the improved algorithm.

关键词： Retrieval Results sort algorithm PageRank HillTop HITS

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Improvement of Counting sorting algorithm

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Journal of Computer and Communications 2023年第10期11卷 12-22页

作者： Chenglong Song Haiming Li School of Computer Science and Technology Shanghai University of Electric Power Shanghai China

By analyzing the internal features of counting sorting algorithm. Two improvements of counting sorting algorithms are proposed, which have a wide range of applications and better efficiency than the original counting sort while maintaining the original stability. Compared with the original counting sort, it has a wider scope of application and better time and space efficiency. In addition, the accuracy of the above conclusions can be proved by a large amount of experimental data.

关键词： sort algorithm Counting sorting algorithms Complexity Internal Features

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A Stable sort algorithm of Two-way Passed and Binary-search Based on Extreme-points(TPBEA)

A Stable Sort Algorithm of Two-way Passed and Binary-search ...

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2014 Asia-Pacific Computer Science and Application Conference(CSAC 2014)

作者： Ruyun Wang Zhentao Zang Yinxian Yu Di Cao College of Harbor Coastal and Offshore EngineeringHohai University

In scientific computation sometimes,it needs to sort the large fragmentcontinuity-sequential *** the characteristics of the data,based on extreme-points,a stable quick sort algorithm for fragment-continuity-sequential data is given by using the binary-search for homing position,two-way passed and directed *** the fragmentcontinuity-sequential data,its efficiency is far higher than that of elementary sorts,and this is about 14 as high as that of Shell's sort and about 47 times as high as that of Heap sort.

关键词： sort algorithm two-way passed directed homing binary-search extreme-point fragmentcontinuity-sequential

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Hybrid sorting algorithm implemented by High Level Synthesis

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IEEE LATIN AMERICA TRANSACTIONS 2020年第2期18卷 430-437页

作者： De Micco, L. Acosta, M. Antonelli, M. Univ Nacl Mar del Plata FIUNMdP CONICET Fac Ingn Inst Invest Cient & Tecnol Elect ICYTE Buenos Aires DF Argentina

This paper proposes a hybrid data ordering algorithm which executes serial and parallel instructions. The implementation of the system is presented in the Zedboard development board of Xilinx that includes a SoC (System on Chip). The design was done in high level language HLS (High Level Synthesis). It receives a vector of N elements and delivers the set of indexes of the L major elements ordered. The complexity of the algorithm is analyzed in a generic way. The required times and resources are evaluated and compared with well known sorting algorithms.

关键词： sort algorithm FPGA implementation Zedboard Hardware Accelerator Compressed Sensing

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Optimized YOLOv2 based vehicle classification and tracking for intelligent transportation system

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RESULTS IN CONTROL AND OPTIMIZATION 2021年 2卷

作者： Kavitha, N. Chandrappa, D. N. SJB Inst Technol Dept ECE Res Ctr Bengaluru India Dayananda Sagar Acad Technol & Management Dept ECE Bengaluru India SJB Inst Technol Bengaluru India

The prevailing real-time system used for vehicle detection and classification using deep learning techniques accuracy diminishes due to its background, illumination variation, occlusion, and variation of vehicle sizes in a scene. Hence the real-time system is proposed to enhance the accuracy level on detection and classification of vehicles for a multi -view surveillance video using an optimized YOLOv2 deep learning algorithm. The improvisation in optimized YOLOv2 has two major stages, firstly to eliminate the influence of background and illumination variation on Focal Loss is improved. Secondly, Rk-means++ clustering discovers the best prior, and normalization to bounding boxes loss to find length and width. Additionally, the network is made more accurate in detecting and classifying vehicles. The experimental results are tested on the COCO-2017 dataset and show that the mean Average Precision (mAP) is 97.85% underneath the premise of substantiating real-time performance. Since there exists a pre -trained model and GPU increases the speed processing to 40 frames per second.

关键词： Deep learning techniques OptimizedYOLOv2 algorithm COCO dataset sort algorithm Vehicle classification Pre-trained model

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Video-based Real-time Intrusion Detection System using Deep-Learning for Smart City Applications 13

Video-based Real-time Intrusion Detection System using Deep-...

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13th IEEE International Conference on Advanced Networks and Telecommunication Systems (IEEE ANTS)

作者： Nayak, Rashmiranjan Behera, Mohini Mohan Pati, Umesh Chandra Das, Santos Kumar NIT Rourkela Dept ECE Rourkela 769008 Odisha India

ISBN: (纸本)9781728137155

There is a huge demand of video surveillance based intelligent security systems which can automatically detect the unauthorized entry or mal-intentional intrusion to the unattended sensitive areas and notify to the concerned authorities in real-time. A novel video-based Intrusion Detection System (IDS) using deep learning is proposed. Here, You Only Look Once (YOLO) algorithm is used for object detection and intrusion is decided using our proposed algorithm based on the shifted center of mass of the detected object. Further, Simple Online and Real-time Tracking (sort) algorithm is used for the tracking of the intruder in real-time. The developed system is also implemented and tested for live video stream using NVIDIA Jetson TX2 development platform with an accuracy of 97% and average fps of 30. Here, the proposed IDS is a generic one where the user can select the region of interest (the area to be intrusion free) of any size and shape from the reference (starting) frame and potential intruders such as a person, vehicle, etc. from the list of trained object classes. Hence, it can have a wide range of smart city applications such as person intrusion free zone, no vehicle entry zone, no parking zone, smart home security, etc.

关键词： Deep learning intrusion detection system transfer learning smart city sort algorithm YOLO algorithm

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Discussion on sort algorithms in Teaching of Data Structure

Discussion on Sort Algorithms in Teaching of Data Structure

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2016 3rd International Conference on Economic,Business Management and Education Innovation(EBMEI 2016)

作者： Bin Yang Wei Zhang Caixia Liu School of Information Science and Engineering Zaozhuang University

sort algorithm is a very important application algorithm. The understanding and mastery of this algorithm is of great help for the future research and work of undergraduate students. However,in reality, understanding of these algorithms is not accurate, and the students could not grasp the idea of the algorithms. In this paper, based on the needs of the construction of Application University, we design a teaching flowchart for the sort algorithm, which has a guiding role for students.

关键词： Data structure sort algorithm Teaching method Application

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