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检索条件"任意字段=2024 3rd International Conference on Image Processing, Object Detection, and Tracking, IPODT 2024"

共 128 条记录，以下是51-60 订阅

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Research on High-Definition Sports Event Video image processing System Based on Computer Shi-Tomas Algorithm 3

Research on High-Definition Sports Event Video Image Process...

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3rd IEEE international conference on Electrical Engineering, Big Data and Algorithms, EEBDA 2024

作者： Ma, Yongping Xinjiang University Urumqi830046 China

ISBN: (纸本)9798350380989

This paper uses Shi-Tomas method for video search of sports competitions. Firstly, the ONVIF356-8950 4K HD network image data decoding processor is selected, and RS485 interface actuator is used as the main interface. Secondly, we build the running environment of sports game video, extract the similar playback screen of sports game video, and identify the boundary of the result. In the tracking link, this method adds a Hima point which has better ability to describe the object and is insensitive to light and rotation. During the operation of the tracking device, the corner points are tracked by optical flow algorithm, and the deviation test is carried out in two directions. Then the target window position is determined according to the remaining fixed corner points. Experiments show that this method can track the motion and deformation of moving objects effectively. The algorithm can effectively eliminate the motion error caused by smooth vertices and improve the stability of the system. The amount of computation improves the real-time performance by about 20% when encountering fast update scenes. This method can improve the search efficiency and shorten the query speed when the search keywords are exactly the same. © 2024 IEEE.

关键词： deviation testing image processing Shi-Tomas algorithm Sports events Video

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Dense object detection for remote sensing images based on multi-scale partitioning and super-resolution optimization 24

Dense object detection for remote sensing images based on mu...

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3rd international conference on Signal processing, Computer Networks and Communications, SPCNC 2024

作者： Li, Lu Zhu, Xia Ni, Shaofeng Gao, Fan State Grid Location based Service Co. Ltd. Beijing China State Grid Information & Telecommunication Group Co. Ltd. Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China

ISBN: (纸本)9798400710834

Detecting dense objects in remote sensing image is a critical yet challenging task due to variations in target scale, image resolution, and complex backgrounds. Traditional detection methods often struggle with these dense regions, particularly for small or overlapping objects. To overcome these limitations, this study introduces an innovative approach that combines multi-scale partitioning with super-resolution optimization to improve the detection of densely packed objects. By applying K-Means clustering to identify regions of interest (ROI) and using a local super-resolution network to enhance these areas, we improve the clarity and detectability of small-scale objects. Additionally, we implement a global-local feature extraction architecture, where global features are captured using Transformer-based attention, while local features focus on fine-grained object details. Non-Maximum Suppression (NMS) is employed to fuse multi-scale features and eliminate redundant detections. Experimental results on the DOTA and HRSC2016 datasets demonstrate the effectiveness of our approach, achieving superior performance over state-of-the-art methods in detecting small and densely packed targets. This method shows significant promise for remote sensing applications requiring high-precision target detection. © 2024 Copyright held by the owner/author(s).

关键词： Proximity sensors

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DEVELOPMENT OF AUTOMATIC TARGET detection SYSTEM FOR 11,000M-CLASS UNDERWATER VEHICLES 43

DEVELOPMENT OF AUTOMATIC TARGET DETECTION SYSTEM FOR 11,000M...

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ASME 43rd international conference on Ocean, Offshore and Arctic Engineering (OMAE)

作者： Kakami, Hitoshi Nakatani, Takeshi Maeda, Yosaku Aso, Tatsuya Shinohara, Yusuke Japan Agcy Marine Earth Sci & Technol 2-15 Natsushima Cho Yokosuka Kanagawa Japan IVIS Inc Bunkyo Ku 3-6-6 Hongo Tokyo Japan

ISBN: (纸本)9780791887813

An underwater vehicle with a tether cable is called a remotely operated vehicle (ROV), and an operator on the mother ship can observe video images and collect samples in real time. However, when operating in very deep sea, the tether cable becomes extremely long, and its handling is technically difficult. Against this background, the authors are currently developing a vehicle that can operate at great depths without relying on tether cables. In this case, the vehicle requires a higher degree of autonomy since the operator cannot remotely control the vehicle. As a technology necessary for autonomous sample collection, we have developed a system that automatically detects and tracks targets from camera images. Machine learning methods are widely used as a means of detecting target objects from camera images. We adopted detection Transformer (DETR) from among multiple methods as a target detection model. We then created an underwater model by training it with videos of deep-sea creatures and dummy targets recorded by our organization. Since camera images in the underwater environment are different from those on land, we applied data augmentation to add lens distortion and blur during training. We also compared multiple methods for tracking models for detected objects and implemented the Byte-Track algorithm. The developed system consists of an underwater camera and an image processing device, each housed in a pressure-resistant container designed to be used at great depths. The underwater camera uses a stereo camera system so that it can detect and track the target and measure its position at the same time. The image processing device was sized to fit in a pressure-resistant container, and hardware capable of image processing using a graphics processing unit (GPU) was selected, and the processing performance was confirmed. After basic confirmation of the developed system through tank tests, it was installed on an 11,000m-class lander owned by our organization and a dem

关键词： Byte-Track computer vision DeepSORT DETR object detection object tracking underwater vehicles YOLO

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E2SIFT: NEUROMORPHIC SIFT VIA DIRECT FEATURE PYRAMID RECOVERY FROM EVENTS 31

E2SIFT: NEUROMORPHIC SIFT VIA DIRECT FEATURE PYRAMID RECOVER...

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2024 international conference on image processing

作者： Henry, Chris Maharjan, Paras Li, Zhu York, George Univ Missouri Dept Comp Sci & Elect Engn Kansas City MO 64110 USA US Air Force Acad Acad Ctr UAS Res Colorado Springs CO 80840 USA

ISBN: (纸本)9798350349405;9798350349399

In recent years, event cameras have achieved significant attention due to their advantages over conventional cameras. Event cameras have high dynamic range, no motion blur, and high temporal resolution. Contrary to traditional cameras which generate intensity frames, event cameras output a stream of asynchronous events based on brightness change. There is extensive ongoing research on performing computer vision tasks like object detection, classification, etc via the event camera. However, due to the unconventional output format of the event camera, it is difficult to perform computer vision tasks directly on the event stream. Mostly, works reconstruct the intensity image from the event stream and then perform such tasks. An important and crucial task is feature detection and description. Scale-invariant feature transform (SIFT) is a widely-used scale-invariant keypoint detector and descriptor that is invariant to transformations like scale, rotation, noise, and illumination. In this work, given an event voxel, we directly generate the LoG pyramid for SIFT keypoint detection. We fit a 3rd-degree polynomial and calculate the polynomial roots to compute the scale-space extrema response for SIFT keypoint detection. Since the extrema computation is performed after LoG thresholding, the solution is computationally less expensive. Experimental results validate the effectiveness of our system.

关键词： neuromorphic vision sensor event camera scale-invariant feature transform SIFT keypoint detection

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Rosette Plant Centre detection and tracking using YOLO: An Efficient Deep Learning Approach 3

Rosette Plant Centre Detection and Tracking using YOLO: An E...

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3rd international conference on Computing and Machine Intelligence (ICMI)

作者： Akagic, Amila Saric, Rijad Buza, Emir Kecman, Stefani Lewsey, Mathew G. Custovic, Edhem Whelan, James Univ Sarajevo UNSA Fac Elect Engn Sarajevo 71000 Bosnia & Herceg La Trobe Inst Sustainable Agr & Food LISAF Dept Anim Plant & Soil Sci Melbourne Vic 3086 Australia La Trobe Univ Australian Res Council Res Hub Med Agr Melbourne Vic 3086 Australia Sci Instruments Australia SIA 2 Res Ave Melbourne Vic 3086 Australia Zhejiang Univ Coll Life Sci State Key Lab Plant Environm Resilience Hangzhou 310058 Peoples R China Zhejiang Univ Prov Int Sci & Technol Cooperat Base Engn Biol Haining 314400 Peoples R China

ISBN: (纸本)9798350372977;9798350372984

The precise detection of plant centres is important for growth monitoring, enabling the continuous tracking of plant development to discern the influence of diverse factors. It holds significance for automated systems like robotic harvesting, facilitating machines in locating and engaging with plants. In this paper, we explore the YOLOv4 (You Only Look Once) real-time neural network detector for plant centre detection. Our dataset, comprising over 12,000 images from 151 Arabidopsis thaliana accessions, is used to fine-tune the model. Evaluation of the dataset reveals the model's proficiency in centre detection across various accessions, boasting an mAP of 99.79% at a 50% IoU threshold. The model demonstrates real-time processing capabilities, achieving a frame rate of approximately 50 FPS. This outcome underscores its rapid and efficient analysis of video or image data, showcasing practical utility in time-sensitive applications.

关键词： Plant Phenotyping Arabidopsis thaliana Computer Vision image processing Deep Learning Neural Networks

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An End-to-End Model for Chicken detection in a Cluttered Environment 13

An End-to-End Model for Chicken Detection in a Cluttered Env...

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13th Iranian/3rd international Machine Vision and image processing conference (MVIP)

作者： Chemme, Komeil Sadeghi Alitappeh, Reza Javanmard Univ Sci & Technol Mazandaran Elect & Comp Engn Dept Behshahr Mazandaran Iran

ISBN: (纸本)9798350350494;9798350350500

Animals in the livestock business need to be seen in real time and online in order for them to grow and develop. The growing global demand for meat and poultry, the development of the livestock and poultry business, and the expansion of their region have made it more difficult for humans to handle the task due to human mistake and the growing amount of work. But thanks to technological breakthroughs like artificial intelligence, learning models, and closed-circuit cameras, most tasks may now be left to robots, preventing human error. This system may be installed and connected worldwide, allowing it to count all animals at any one time and give us information on each animal's number and location. In this research, we created a model consisting of a segmentation module that readies input data for a detector model based on YOLOv8. In order to study chicken behavior, an integrated end-to-end approach is utilized to track and count the birds. The experimental results provide insight into the new proposal's accuracy performance compared to earlier methods, demonstrating an 8% improvement. The goal of this article is to teach a YOLOv8-based learning model that may be applied to the development of an automated, integrated system for counting and assessing animal welfare in the future.

关键词： Chicken detection object detection Deep learning Segmentation

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Research on ladle target detection and spatial positioning methods 24

Research on ladle target detection and spatial positioning m...

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3rd international conference on Computer, Artificial Intelligence and Control Engineering, CAICE 2024

作者： Qiu, Yaojun Northeastern University Shenyang China

ISBN: (纸本)9798400716942

At present, the research on ladle detection and positioning technology in the metallurgical industry is increasingly attracting the attention of industry experts. Ladle detection and positioning can not only provide real-time information for the tracking, operation monitoring and early warning of the entire production process, but also are necessary boundary condition parameters for detecting multiple physical field parameters such as ladle temperature and thickness. In recent years, with the successful application of computer vision technology in target tracking, image recognition, and various physical parameter detection, vision based detection and positioning technology has provided a good technical support for the solution of ladle detection and positioning problems, and a feasible solution for overcoming detection and positioning in harsh high-temperature environments on site. This article is the first to use computer vision technology to solve the problem of detecting and locating steel ladles in the metallurgical industry. In terms of ladle detection, improvements have been made on the YOLO algorithm, proposing YOLO-separation, which separates the target from the background and significantly reduces the impact of complex backgrounds in metallurgical industry sites on experimental accuracy;In terms of ladle positioning, the ORB algorithm based on FAST feature points and BRIEF descriptors, as well as the FLANN feature matching algorithm, are adopted. The experimental results show that the error of this algorithm in calculating the position of the ladle is about 4.7%. The research results indicate that the error of this algorithm is within a reasonable range, reducing manual labor and increasing intelligence in the metallurgical process, which is very practical. © 2024 ACM.

关键词： Convolutional neural network image processing object detection Spatial localization YOLO

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A Single Stage Multitasking Model for Person detection and Activity Recognition 3

A Single Stage Multitasking Model for Person Detection and A...

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3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Chemmanam, Ajai John Jose, Bijoy A Moopan, Asif Cochin University of Science and Technology CPS Lab Department of Electronics Kerala India Cochin University of Science and Technology CPS Lab Department of Computer Science Kerala India Vuelogix Technologies Pvt. Ltd Kerala India

ISBN: (纸本)9798350372120

The integration of computer vision technologies into our daily lives has become increasingly prevalent, finding applications in diverse fields such as surveillance and public safety. A crucial aspect of these applications involves the detection of individuals, tracking their movements, and recognizing their activities. Traditional approaches often employ separate specialised models for each of these tasks, resulting in complex video processing pipelines that may strain computational resources, particularly in scenarios where real-time analytics is imperative. This work explores single stage multitasking architecture for person detection and activity recognition, without any explicit tracking, segmentation or body keypoint ***-time action recognition is a challenging task. Online/realtime models cannot leverage information from future frames, hence it cannot perform equally well like the offline/non-realtime models. The actions may be incomplete or look very similar at early stages. The actions can be of varying duration, and multiple people will be performing same or different actions in a video. Despite these challenges, the proposed model achieved 19.17mAP on the AVA dataset v.2 at 36 frames per second (FPS) and an optimised version runs at 51 FPS with the same accuracy levels on an RTX 3090 GPU. We were able to optimise the video processing pipeline by eliminating the need for multiple specialized models. © 2024 IEEE.

关键词： object detection

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Raspberry Pi based Color and Shape detection Bot 3

Raspberry Pi based Color and Shape Detection Bot

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3rd IEEE international conference on Intelligent Techniques in Control, Optimization and Signal processing, INCOS 2024

作者： Pon Pushpa, S. Ewins Mariammal, K. Kumar, Dharavath Sai Mohamed Nishar, M. Siddharth, K. Sundhara Ganesh, J. College of Engineering Guindy Anna University Department of Ece Chennai India Madras Institute of Technology Anna University Department of Electronics Engg. Chrompet Chennai India

ISBN: (纸本)9798350361186

This paper introduces an innovative sorting system that leverages, advanced image processing capabilities, particularly focusing on color and shape detection, integrated into a robotic platform. The cost-effective solution, utilizes the Raspberry Pi single-board computer to maximize computational power, achieving efficient object sorting. The system excels in identifying, sorting, and processing objects, based on color and shape attributes, with added functionality for obstacle detection. Through seamless integration of Raspberry Pi with Python programming, and advanced image processing, the project showcases the potential of this approach, in addressing challenges in automation and robotics. This technology holds promise for future applications, in medical imaging and pharmaceutical settings, offering efficient analysis and sorting based on color and shape characteristics. © 2024 IEEE.

关键词： Color

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Research on vehicle object detection in Aerial Photography Based on Improved YOLO Model 3

Research on vehicle Object Detection in Aerial Photography B...

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3rd IEEE international conference on Electrical Engineering, Big Data and Algorithms, EEBDA 2024

作者： Wang, Xiangda Wang, Yuhao He, Ang Yang, Renyu School of Mechatronic Engineering and Automation Shanghai University Shanghai China College of Engineering Science and Technology Shanghai Ocean University Branch Shanghai China College of Information Engineering Shanghai Maritime University Shanghai China Business School University of Shanghai for Science and Technology Shanghai China

ISBN: (纸本)9798350380989

The historical mission of transportation personnel is to build a strong transportation nation. The most important component of an intelligent transportation system is vehicle detection and analysis, which involves gathering information and monitoring the status of vehicles using advanced technologies to achieve effective allocation of transportation resources. In recent years, methods based on convolutional neural networks have made rapid advancements in the field of image processing. Addressing the challenges in small object detection, this paper proposes an improved algorithm for small object detection based on YOLOv3. Building upon the single-stage object detection algorithm YOLOv3, several improvements are made. Firstly, a modified K-means clustering algorithm is used to analyze and generate anchor boxes, providing an improved evaluation metric. Secondly, addressing the weak feature extraction capability and low detection accuracy of the backbone network for small objects, a lightweight network called EfficientNet is employed. EfficientNet-B6 is selected as the backbone network for experimental analysis based on the characteristics of the dataset. The proposed algorithm is validated using public datasets and compared with the improved Faster R-CNN algorithm, demonstrating that the improved YOLOv3 is suitable for real-time object detection. The detection accuracy achieved is 86.8%, with a detection speed of 39.5 frames per second on a 16GB GPU. © 2024 IEEE.

关键词： convolutional neural networks EfficientNet object detection YOLOv3

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