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检索条件"任意字段=Conference on Real-Time Image and Video Processing 2011"

共 8693 条记录，以下是131-140 订阅

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3D Convolutional Spiking Neural Network for Human Action Recognition Using Modulating STDP With Global Error Feedback 18

3D Convolutional Spiking Neural Network for Human Action Rec...

引用

18th Annual IEEE International Systems conference (SysCon)

作者： Nawarathne, Thoshara Leung, Henry Univ Calgary Dept Elect & Comp Engn Calgary AB Canada

ISBN: (纸本)9798350358810;9798350358803

video action recognition using 3D Convolutional Neural Networks (CNN) become an increasingly popular strategy in past years with the evolution of machine learning and computer vision. However, the higher memory and computation capacity requirement of these networks leads to the use of low-power memory-saving neural networks to perform video action recognition tasks efficiently. Spike-based information processing and computation of bio-inspired spiking convolutional neural networks perform an essential role when comes to energy efficient memory saving computation for video classification and action recognition tasks which allow on-chip real-time processing. This paper proposes a novel 3D Convolutional Spiking Neural Network (CSNN) architecture with modulating STDP supervised learning via global error feedback for human action recognition in video data. The proposed model includes two 3D convolutional layers, followed by two spiking neuron layers, modeled using Leaky Integrate and Fire (LIF) neurons for feature extraction from video data. Using the modulating STDP learning rule with global error feedback, this model can successfully recognize human actions from video data allowing online parallel computations. The proposed network experimented on two datasets: one 3D image dataset - synthesized 3D MNIST and one video dataset - UCF 101 human action recognition dataset and achieved 71.6% and 63.7% recognition accuracy.

关键词： Asynchronous video 3D convolutional spiking neural networks action recognition UCF 101 dataset

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FPGA-based real-time video Quality Enhancement for Unmanned Aerial Vehicle 20

FPGA-based Real-time Video Quality Enhancement for Unmanned ...

引用

20th IEEE International conference on Mechatronics and Automation, ICMA 2023

作者： Lu, Yue Li, Jinglei Yang, Qinghai Xidian University School of Telecom. Engineering Xi'an China

ISBN: (纸本)9798350320831

With the rapid improvement of UAV imaging equipment in terms of image resolution and video frame rate, relying solely on software to enhance images with extremely high data volume is no longer sufficient to meet real-time requirements. Therefore, a new method of processing image signals utilizing field programmable gate array (FPGA) hardware is proposed. This method involves analyzing traditional image enhancement algorithms to solve problems such as incorrect frame mapping, low contrast image enhancement, and incomplete comparison image enhancement mechanism resulting in noise during the engineering process. A limited contrast enhancement mechanism is also proposed to suppress the appearance of enhancement noise. Using the FPGA system, a hardware optimization design method is employed, including parallel processing, pipeline operation, and equivalent conversion, to achieve hardware image enhancement with extremely low resource consumption. This allows for the realization of video image enhancement with a resolution of 1920∗1080 and a frame rate of 100Hz. The system has improved the overall brightness of images, target contrast, and image information restoration, and has achieved good results. This algorithm can be widely used in engineering design. © 2023 IEEE.

关键词： image enhancement

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Enhancing video Surveillance with Deep Learning-Based real-time Handgun Detection and Tracking 1

引用

8th International conference on Computer Vision and image processing (CVIP)

作者： Yadav, Pavinder Gupta, Nidhi Sharma, Pawan Kumar Natl Inst Technol Hamirpur Hamirpur 177005 Himachal Prades India Natl Inst Technol Kurukshetra Kurukshetra 136119 Haryana India

ISBN: (数字)9783031581816

ISBN: (纸本)9783031581809;9783031581816

Handguns, pistols, and revolvers are commonly used in today's world for committing criminal acts, requiring the need for effective surveillance and control systems. However, despite the advancement of security systems, human monitoring and involvement are still necessary to effectively combat these crimes. This paper provides a robust automated handgun identification technique for recorded videos and live CCTV footage that may be used for both control and surveillance purposes. Automatic detection of firearms is crucial for improving people's protection and safety, however, it is a challenging task because of the numerous differences in design, size, and appearance of firearms. In recent years, object detectors have improved, yielding better findings and shorter inference times. The authors used cutting-edge object detector YOLOv7 for firearm detection. A varied and demanding dataset of 15,367 images for weapon identification is also proposed, which is carefully annotated for weapon localization and classification. After analysing the data, it is determined that the model achieves an accuracy rate of 96.80% and recall rate of 90.37%.

关键词： Deep Learning Machine Learning Weapon Detection Surveillance System YOLO

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Optimizing Urban Traffic with image processing: A New Era of Management

Optimizing Urban Traffic with Image Processing: A New Era of...

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2025 International conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Kumar, Alok Palati, Madhu Tripathi, Arijeet Sagar, Swapnil Seegi, Vishwa M. Bms Institute of Technology and Management Dept. of Eee Bangalore India

ISBN: (纸本)9798331538538

Urban traffic management is a crucial concern in today's cities due to increased vehicle density and the complexity of transport networks. Effective real-time vehicle tracking systems are critical for reducing traffic congestion, improving road safety, and optimizing traffic flow. The suggested system uses real-time video feeds from cameras strategically deployed throughout metropolitan traffic networks. YOLOv5 recognizes and localizes vehicles inside video frames, while Alex Net V3 classifies the observed vehicles as automobiles, busses, trucks, and motorcyclists. The system continuously refreshes traffic data, providing information about traffic density, flow patterns, and potential bottlenecks. This data is integrated into traffic management systems to enable adaptive signal control and dynamic route planning. This greatly increased the precision and efficiency of urban traffic management, providing a scalable solution to the mounting issues of urbanization. © 2025 IEEE.

关键词： Traffic congestion

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real-time Multi-Camera video Stitching Algorithm with Adaptive Sensing of Illumination and Dynamic Objects 4

Real-Time Multi-Camera Video Stitching Algorithm with Adapti...

引用

4th Asia-Pacific conference on Communications Technology and Computer Science, ACCTCS 2024

作者： Lei, Min He, Lijun Li, Fan Xi'an Jiaotong University Department of Information and Communications Engineering Xi'an China

ISBN: (纸本)9798350359985

With the development of digital image processing technology, the demand and application of wide-view video continue to grow. However, the existing stitching algorithms may result in unnatural appearance, misalignment, and ghosting issues when stitching scenes with inconsistent brightness or dynamic objects. To address the above problems, we propose a real-time multi-camera video stitching algorithm with adaptive sensing of illumination and dynamic objects. Within the proposed illumination compensation module, brightness equalization across varying illumination conditions is attained through the application of gain compensation to image blocks. We propose a histogram-based dynamic object detection approach to dynamically detect the presence of moving objects along the stitching seam. This method facilitates the adaptive detection of dynamic entities by analyzing the histogram disparities between the local vicinity of the stitching seam and the broader image context. The experimental results show that it can effectively solve the problem of unnatural stitching results arising from the uneven brightness of the image, and can effectively detect the presence of dynamic objects near the stitching seam so that the effect of the video stitching has been improved. © 2024 IEEE.

关键词： Object detection

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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...

引用

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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Hybrid Quantum-Classical Transfer Learning for real-time Data processing 17

Hybrid Quantum-Classical Transfer Learning for Real-Time Dat...

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17th International conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Chittem, Mahesh Babu Kumar Pradhan, Ashok SRM University AP Dept of Computer Science and Engineering Amaravati India

ISBN: (纸本)9798331531195

Transfer learning is a set of techniques to apply skills or knowledge from a source task to a target task that is different but related, while Hybrid Quantum-Classical Transfer Learning (HQCTL) model extends the skills learned with quantum feature extraction specifically for edge computing which lacks resources. HQCTL combined with quantum-derived characteristics enhances accuracy, time, and real-time computation when it comes to classical aspects such as object detection or image analysis. In experimenting with images datasets such as COCO and PASCAL VOC the distribution of the framework generally presented higher accuracy and lower costs in terms of computation compared to either a purely classical or quantum approach. Of course, quantum enhanced feature extraction is still far from known and has greater potential for HQCTL as it helps to advance data representation which is optimal for the strict real-time processing in the IoT periphery. Possible research avenues include the development of different quantum representations of the problem, enhancements of the approach interconnectivity with various edge substrates, and the application of the framework to new machine learning tasks such as video analysis and time series prediction. Through presenting the HQCTL framework the potential of hybrid quantum-classical models to enhance edge AI applications while offering reliability, scalabilty and efficiency is demonstrated. © 2025 IEEE.

关键词： Transfer learning

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Virtual Gym Tracker: AI Pose Estimation 2

Virtual Gym Tracker: AI Pose Estimation

引用

2nd IEEE International conference on Advances in Information Technology, ICAIT 2024

作者： Rane, Milind Date, Ameya Deshmukh, Vaishnavi Deshpande, Prutha Dharmadhikari, Aryan VIT Pune India

ISBN: (纸本)9798350383867

This research paper presents a novel Virtual Gym Tracker AI Pose Estimation system designed to enhance virtual fitness experiences. Leveraging advanced deep learning techniques and real- time image analysis, the system accurately tracks and reconstructs user movements in real time. It offers precise pose estimation, enabling form correction, performance analysis, and interactive coaching. Extensive experiments confirm its accuracy and adaptability across diverse exercise scenarios, making it a valuable tool for virtual gym environments. © 2024 IEEE.

关键词： AI deep-learning video - processing Virtual Gym Tracker

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image Monitoring System Based on Deep Neural Network 2

Image Monitoring System Based on Deep Neural Network

引用

2nd International conference on image processing, Computer Vision and Machine Learning, ICICML 2023

作者： Weng, Junhong Zeng, Lingfeng Song, Yingjie Shenzhen Power Supply Bureau CO. LTD China Southern Power Grid Guangdong Shenzhen China

ISBN: (纸本)9798350331417

For safety and security reasons, the indoor/outdoor working environments of various industries require the use of many cameras for automated surveillance. In such context, a major challenge for automated monitoring system is achieving high-precision real-time performance for image classification and object detection. In this paper, we present a novel image surveillance system based on a combined approach derived from YOLO V5. The system first detects moving targets using background subtraction. Then, we propose a modified YOLO V5 algorithm for accurately detecting and categorizing different objects in images captured in a video stream. The system runs in real time and could analyze multiple video streams simultaneously. The results of the experiments show that this system has good performance and could be widely applied on several areas, such as security, surveillance, and traffic management. © 2023 IEEE.

关键词： deep nueral network monitoring system YOLOv5

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VidProM: A Million-scale real Prompt-Gallery Dataset for Text-to-video Diffusion Models 38

VidProM: A Million-scale Real Prompt-Gallery Dataset for Tex...

引用

38th conference on Neural Information processing Systems, NeurIPS 2024

作者： Wang, Wenhao Yang, Yi University of Technology Sydney Australia Zhejiang University China

The arrival of Sora marks a new era for text-to-video diffusion models, bringing significant advancements in video generation and potential applications. However, Sora, along with other text-to-video diffusion models, is highly reliant on prompts, and there is no publicly available dataset that features a study of text-to-video prompts. In this paper, we introduce VidProM, the first large-scale dataset comprising 1.67 Million unique text-to-video Prompts from real users. Additionally, this dataset includes 6.69 million videos generated by four state-of-the-art diffusion models, alongside some related data. We initially discuss the curation of this large-scale dataset, a process that is both time-consuming and costly. Subsequently, we underscore the need for a new prompt dataset specifically designed for text-to-video generation by illustrating how VidProM differs from DiffusionDB, a large-scale prompt-gallery dataset for image generation. Our extensive and diverse dataset also opens up many exciting new research areas. For instance, we suggest exploring text-to-video prompt engineering, efficient video generation, and video copy detection for diffusion models to develop better, more efficient, and safer models. The project (including the collected dataset VidProM and related code) is publicly available at https://*** under the CC-BY-NC 4.0 License. © 2024 Neural information processing systems foundation. All rights reserved.

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