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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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Phased array ultrasonic S-scan testing of near-detection-surface defects based on a background subtraction algorithm

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MATERIALS RESEARCH EXPRESS 2022年第3期9卷 036507-036507页

作者： Tian, Yukuo Xu, Guocheng Wei, Lingbo Zhou, Guanghao Lin, Yuting Fan, Qiuyue Gu, Xiaopeng Jilin Univ Minist Educ Key Lab Automobile Mat Changchun 130025 Peoples R China Jilin Univ Dept Mat Sci & Engn Changchun 130025 Peoples R China China FAW Co Ltd Jiefang Business Div 1063 Chuangye St Changchun 130011 Peoples R China CNR Changchun Railway Vehicles Co Ltd Changchun 130062 Peoples R China Fujian Prov Key Lab Welding Qual Intelligent Eval Longyan 364012 Peoples R China

In phased array ultrasonic sector scanning testing, echoes from defects near the detection surface are often overlapped with interface echoes and cannot be characterized. Based on linear acoustic theory, a mathematical model combining the background subtraction and the sum of squares differences algorithm is established to extract the characteristics of near-detection-surface defect echoes. The upper surface of the artificial notch defect is used as a near-detection-surface defect for detection. Specially, linear interpolation algorithm is used to suppress the effect of residual interface echo on the defect echo feature extraction and the effect of different interpolation factors is analyzed. The defect location and size were calculated based on the extracted defect characteristics. The simulation and experimental results indicate that the model can effectively extract the near-detection-surface defect echoes characteristics. Furthermore, when the interpolated signal reaches at least 500 MHz, residual interface echoes can be effectively suppressed. More importantly, according to the extracted defect echo characteristics, the localization and quantitative accuracies of near-surface defects can reach 0.2 mm and 0.3 mm, respectively. These findings provide an effective strategy for phased array ultrasonic sector scanning testing of near-detection-surface defects.

关键词： phased array ultrasonic nondestructive testing sector scanning imaging background subtraction algorithm localization and quantification near-detection-surface defects

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Assessment of rib spalling hazard degree in mining face based on background subtraction algorithm and support vector machine

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CURRENT SCIENCE 2019年第12期116卷 2001-2012页

作者： Si, Lei Wang, Zhongbin Liu, Xinhua Tan, Chao Xu, Rongxin China Univ Min & Technol Sch Mechatron Engn Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Jiangsu Engn Technol Res Ctr Intelligent Equipmen 1 Daxue Rd Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Jiangsu Key Lab Mine Mech & Elect Equipment 1 Daxue Rd Xuzhou 221116 Jiangsu Peoples R China

Rib spalling is one of the common hazards in a fully mechanized mining face. In order to accurately assess the hazard degree, this study proposes a new method based on background subtraction algorithm and support vector machine (SVM). First, the architecture diagram of rib spalling feature analysis is constructed, and the rib spalling feature indices are determined, including the duration area, height and the centre of ribs spalling height. Then, the specific feature analysis process of rib spalling is performed using the background subtraction algorithm. Furthermore, some virtual 3D rib spalling animations are generated using 3D Studio Max (3Ds Max) software to verify the reasonability of extracted features. Thereafter, the assessment model of rib spalling hazard degree is established based on SVM. Three assessment models based on SVM, back propagation neural network (BP-NN) and artificial immune (AI) algorithm have been developed. The assessment accuracy of SVM (reaching 85%) is obviously higher than that of BP-NN (75%) and AI (70%) algorithm. The results indicate the feasibility and superiority of the proposed method in the assessment of rib spalling hazard degree.

关键词： background subtraction algorithm hazard degree assessment mining face rib spalling support vector machine

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Detecting Moving object using background subtraction algorithm in FPGA 4

Detecting Moving object using Background Subtraction Algorit...

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4th International Conference on Advances in Computing and Communications (ICACC)

作者： Gujrathi, Poonam Priya, R. Arokia Malathi, P. DYPCOE Dept E&TC Engn Pune Maharashtra India

ISBN: (纸本)9781479943630

Image and video processing applications tend to be real time constraints. Applications like visual surveillance, traffic monitoring, vehicle tracking, autonomous navigation;computer vision etc. has the basic requirement of identifying moving object in real time. Hardware based approaches are well suited for real time motion detection as they results in high performance and low cost. For rapid development of real time motion detection systems, we propose hardware architecture for motion detection based on the background subtraction algorithm, which is implemented in FPGAs. The steps involved in the process are: (a) a grey level background image is stored in an SRAM memory in FPGA, (b) color reduction is applied to both the background and current images, (c) Both filtered images are then subtracted using image subtraction (d) the gravity center of the object of resultant image is calculated and sent to a PC (via RS-232 interface) (e) Sobel edge detection algorithm is used to identify object's edges. Identifying object's edge could be extended by classifying objects based on their shapes.

关键词： Motion Detection background subtraction algorithm Segmentation FPGA Sobel edge detection

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Identification and counting of fish targets using adaptive resolution imaging sonar

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JOURNAL OF FISH BIOLOGY 2024年第2期104卷 422-432页

作者： Shen, Wei Peng, Zhanfei Zhang, Jin Shanghai Ocean Univ Coll Marine Sci Shanghai Peoples R China Shanghai Ocean Univ Shanghai Estuary Marine Surveying & Mapping Engn T Shanghai Peoples R China Shanghai Ocean Univ Coll Marine Sci Shanghai 201306 Peoples R China

Fish are a critical component of marine biology;therefore, the accurate identification and counting of fish are essential for the objective monitoring and assessment of marine biological resources. High-frequency adaptive resolution imaging sonar (ARIS) is widely used for underwater object detection and imaging, and it quickly obtains close-up video of free-swimming fish in high-turbidity water environments. Nonetheless, processing the massive data output using imaging sonars remains a major challenge. Here, the authors developed an automatic image-processing programme that fuses K-nearest neighbour background subtraction with DeepSort target tracking to automatically track and count fish. The automatic programme was evaluated using four test data sets with different target sizes and observation ranges and differently deployed sonars. According to the results, the approach successfully counted free-swimming fish targets with an accuracy index of 73% and a completeness index of 70%. Under appropriate conditions, this approach could replace time-consuming semi-automatic approaches and improve the efficiency of imaging sonar data processing, while providing technical support for future real-time data processing.

关键词： acoustic image background subtraction algorithm fish target target identification

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Extraction algorithm for longitudinal and transverse mechanical information of AFM

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Nanotechnology and Precision Engineering 2022年第2期5卷 27-37页

作者： Chunxue Hao Shoujin Wang Shuai Yuan Boyu Wu Peng Yu Jialin Shi School of Information and Control Engineering Shenyang Jianzhu UniversityShenyang 110168China State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China Institute of Robotics and Intelligent Manufacturing Innovation Chinese Academy of SciencesShenyang 110169China

The atomic force microscope(AFM)can measure nanoscale morphology and mechanical properties and has a wide range of *** traditional method for measuring the mechanical properties of a sample does so for the longitudinal and transverse properties separately,ignoring the coupling between *** this paper,a data processing and multidimensional mechanical information extraction algorithm for the composite mode of peak force tapping and torsional resonance is *** the basis of a tip–sample interaction model for the AFM,longitudinal peak force data are used to decouple amplitude and phase data of transverse torsional resonance,accurately identify the tip–sample longitudinal contact force in each peak force cycle,and synchronously obtain the corresponding characteristic images of the transverse amplitude and *** results show that the measured longitudinal mechanical characteristics are consistent with the transverse amplitude and phase characteristics,which verifies the effectiveness of the ***,a new method is provided for the measurement of multidimensional mechanical characteristics using the AFM.

关键词： Atomic force microscope Peak force tapping Torsional resonance Mechanical characteristic measurement background subtraction algorithm Coupled mechanical model

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Dynamic Recognition and Tracking of Barium Flow Field Based on Deglutition Video

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JOURNAL OF WEB ENGINEERING 2021年第2期20卷 533-555页

作者： Qin, Guofeng Zou, Jianhuang Xia, Qiufang Qin, Jiahao Tongji Univ Dept Comp Sci & Technol Shanghai Peoples R China Shanghai First Rehabil Hosp Shanghai Peoples R China

Dynamic fluoroscopy was used to study swallowing in 84 adult patients. We proposed a method to extract the barium contrast region by improved interframe difference method, and to indirectly determine the position of epigmatous cartilage and cricopharyngeal muscle according to the location of barium meal. The method is easy to understand, and the extraction effect is good, with 85% probability of successful extraction. On the other hand, in order to evaluate the degree of deglutition difficulty, we used calculation to evaluate variables including displacement, duration, residual quantity, etc., except that there were gender differences in variables and external factors, such as illumination, most of the measurement variables had very good reliability. The experimental results showed that the moving target fluid barium was extracted by quantifying dynamic fluorescence deglutition and using gaussian based background subtraction algorithm. We conclude that this approach significantly reduces the time it takes clinicians to examine moving images. This paper describes how to study swallowing disorders by X-ray barium fluoroscopy, explains the application of interframe difference algorithm and background subtraction in deglutiography, and extracts the residual amounts in three locations: oral cavity, epiglottic cartilage and piriform fosse.

关键词： Dynamic fluoroscopy X-ray barium fluoroscopy swallowing disorders interframe difference algorithm background subtraction algorithm

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Research on Motion Target Detection and Tracking algorithm Based on Complex Scene

Research on Motion Target Detection and Tracking Algorithm B...

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第37届中国控制会议

作者： Jin Xue-lian Yang Jian-hua Lu Wei Faculty of Electronic Information and Electrical Engineering Dalian University of Technology

In order to solve the problem of abnormal video monitoring in coal seam gas mining field,a moving target detection and tracking algorithm based on complex scene is *** this method,a segmentation algorithm based on three-frame difference method is used to divide the image into a static background region and a dynamic background region that includes the reciprocating motion of the water *** the static background area,the three-frame difference method and the background subtraction algorithm are used to merge the segmentation foreground,and the three-frame difference method is used to segment the foreground for the dynamic background area,and the foreground target is tracked by using the Kalman tracking *** show that the algorithm can accurately detect the moving target and track it in the CBM mining scene to meet the real-time requirements of video monitoring.

关键词： coal seam gas mining complex scene three-frame difference method background subtraction algorithm Kalman tracking algorithm

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Smart Surveillance Monitoring System 1

Smart Surveillance Monitoring System

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1st IEEE International Conference on Data Management, Analytics and Innovation (ICDMAI)

作者： Jain, Akshat Basantwani, Shraddha Kazi, Owais Bang, Yogita Pune Inst Comp Technol Dept Comp Engn Pune Maharashtra India

ISBN: (纸本)9781509040834

In today's world, where everyone wants to keep their valuables safe and secure, video surveillance for observing a particular area has become the need of the hour. To address this problem, we have come up with a solution of smart surveillance system for certain places like bank vaults, homes where the human presence is not available. At such places, it is not worth to continuously monitor the area with the cameras. This wastes the power consumption as well as the storage required for the footages. Our system will detect human presence using PIR sensor. Raspberry Pi operates and controls motion detecting sensors and video cameras for remote sensing and surveillance, streams live video and records it for future playback. On detection of any movement the cameras will trigger the surveillance. The proposed system captures information and transmits it via Internet to smartphones and laptops. Live streaming is done throughout the WAN i.e. also available within different LANs. Extensive human monitoring of the incoming video channels is impractical, expensive and ineffective. Using a universal sample-based background subtraction algorithm called ViBe (Visual background Extractor), we detect motion in red alert zone i.e. a place in the room where the valuables are placed, for any suspicious activity and we send an alarm to the user. The video recorded and stored can be used to identify the intruder and help in catching him. To preserve privacy and securely transmit the video footage over the network, we are using blowfish encryption and decryption algorithm to provide extra security from hackers.

关键词： Surveillance IoT Streaming Security background subtraction algorithm Encryption Decryption Blowfish algorithm

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