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4th International conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2024

作者： Wang, Di Yang, Zhonglin Liu, Gaotian China North Vehicle Research Institute Fourth Technology Department Beijing China

ISBN: (纸本)9798331531225

Intelligent recognition algorithms deployed on edge devices offer strong real-time processing capabilities and high security for online video image analysis. However, real-time video image recognition remains challenging, particularly when deploying computationally intensive action recognition models on resource-constrained edge devices. This paper introduces a dual-network human action recognition method that leverages the strengths of both 2D and 3D convolutional neural networks (CNNs). Initially, a 2D CNN detects hu-man targets, followed by the generation of time-sequence data for these tar-gets using a matching method. Human action recognition is then achieved through a 3D CNN. This method addresses the challenges of extracting and modeling temporal action characteristics, enabling accurate human action localization and classification. Five distinct action recognition datasets were constructed to train the 3D CNN, which were subsequently deployed on an edge intelligent platform. Empirical testing of the action recognition system demonstrated a recognition speed of 10 frames per second, with accuracy exceeding 80% for certain action categories. © 2024 IEEE.

关键词： video analysis

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LIGHTING image/video STYLE TRANSFER METHODS BY ITERATIVE CHANNEL PRUNING 49

LIGHTING IMAGE/VIDEO STYLE TRANSFER METHODS BY ITERATIVE CHA...

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49th IEEE International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Wu, Kexin Tang, Fan Liu, Ning Deussen, Oliver Le, Thi-Ngoc-Hanh Dong, Weiming Lee, Tong-Yee Jilin Univ Jilin Jilin Peoples R China ICT CAS Beijing Peoples R China Midea Grp Beijiaozhen Peoples R China Univ Konstanz Constance Germany Natl Cheng Kung Univ Tainan Taiwan CASIA Beijing Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Deploying style transfer methods on resource-constrained devices is challenging, which limits their real-world applicability. To tackle this issue, we propose using pruning techniques to accelerate various visual style transfer methods. We argue that typical pruning methods may not be well-suited for style transfer methods and present an iterative correlation-based channel pruning (ICCP) strategy for encoder-transform-decoder-based image/video style transfer models. The correlation-based channel regularization preserves the feature distributions for content and style references, and the iterative pruning strategy prevents layer collapse when pruning on the encoder-decoder structure. Experiments demonstrate that the proposed ICCP can generate visual competitive results compared to SOTA style transfer methods and significantly reduces the number of parameters (at least 70K) and inference time. Model is available at https://***/wukx-wukx/ICCP.

关键词： visual style transfer model pruning

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video Analytics with FPGA based smart cameras for Object Recognition in the game of field hockey 1

Video Analytics with FPGA based smart cameras for Object Rec...

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1st IEEE International conference on Networking and Communications, ICNWC 2023

作者： Praveena, M. Vinoth Thyagarajan, V. Venkatasubramani, V.R. Thiagarajar College of Engineering Dept of ECE Tamilnadu Madurai India

ISBN: (纸本)9798350336009

video Analytics is an image processing method that takes video as input and extracts information. It is the latest technology that analyzes and processes a digital video signal for real time monitoring. Some of the countries of south and eastern hemisphere are top at field hockey because they are very much developed in technology. Even though, field hockey is our national game we are not in the top because of lack of technology. By using video analytics, we can analyze the player's video and from that they can improve their performance. Not only performance analysis there are many advantages like weapon detection, missing person in stadium, replay of players, real time instant videos, scores, reconstruct story of a match, violent detection can be analyzed. The experimental setup for this work is camera with SD-card, VGA interfacing with Field Programmable Gate Array (FPGA). The SD-card communicates with FPGA using SD-card interface. image datas stored in SD-card are read by FPGA and stored in its memory. Then that image is processed using Convolutional Neural Network (CNN). This algorithm detects the objects in an image. Then object recognition is performed for videos to make decisions. This whole processing of image is performed inside FPGA. Finally, to display the processed image in monitor, FPGA communicates with VGA interface through VGA cable. real time videos will be displayed by increasing the frame rate. Our work is implemented in Altera DE 1 FPGA board of device EP2C20F484C7N which comes under family of cyclone II and image displayed on monitor with resolution and frequency of 1366x768-60 Hz. © 2023 IEEE.

关键词： Cameras

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Depth-Aware Weather-Enhanced 3D Object Detection for Roadside Infrastructure 8

Depth-Aware Weather-Enhanced 3D Object Detection for Roadsid...

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8th International conference on video and image processing, ICVIP 2024

作者： Li, Yanfei Zeng, Yuan Gong, Yi Department of Electronics and Electrical Engineering Southern University of Science and Technology China College of Big Data and Internet Shenzhen Technology University China

ISBN: (数字)9781510689244

ISBN: (纸本)9781510689237

Monocular 3D object detection is a crucial topic in autonomous driving and Intelligent transportation systems (ITS). Most existing methods are evaluated on clean datasets but exhibit arresting performance degradation in the real world with varying weather conditions. image enhancement methods have been introduced in existing vehicle-centric works to address the impact of diverse weather on detection performance and improve the robustness of the model. Unlike vehicle-centric systems, roadside infrastructure can enlarge perception range and is less affected by vehicle occlusion to increase the response time in case of danger. To enhance the robustness of detection models on roadside infrastructure, this work introduces an image enhancement method incorporating depth information in modeling climate changes. We use a classification network trained on real weather data to validate the effectiveness of the enhancement and use state-of-the-art roadside infrastructure monocular detection models to evaluate the effectiveness of our method on object detection. Extensive experiments demonstrate that our method can enhance the robustness and generalization of detection models under various weather conditions. © 2025 SPIE.

关键词： Object detection Rain image enhancement Data modeling Education and training Cameras 3D modeling Adverse weather Performance modeling image contrast enhancement

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image Quality Improvement and Reduction of Parameters by Utilizing Learning-based Noise Reduction Method for Surveillance Camera images 24

Image Quality Improvement and Reduction of Parameters by Uti...

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6th International conference on Intelligent Medicine and image processing, IMIP 2024

作者： Goto, Tomio Department of Computer Science Nagoya Institute of Technology Japan

ISBN: (纸本)9798400710032

It is well known that video images captured by in-vehicle cameras and surveillance cameras are degraded due to noise introduced by their shooting environments. In this paper, we remove such noise using a learning method to improve the image quality. Furthermore, we propose a practical method for real-time processing by making the process lighter and faster. In order to make practical use of denoising techniques, it is necessary to develop lightweight models with high denoising performance. Experimental results show the effectiveness of our proposed method. © 2024 ACM.

关键词： image quality

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A real-time Network for Fast Breast Lesion Detection in Ultrasound videos 6th

A Real-Time Network for Fast Breast Lesion Detection in Ultr...

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6th Chinese conference on Pattern Recognition and Computer Vision (PRCV)

作者： Dai, Qian Lin, Junhao Li, Weibin Wang, Liansheng Xiamen Univ Sch Informat Xiamen Peoples R China Xiamen Univ Sch Med Xiamen Peoples R China

ISBN: (纸本)9789819985579;9789819985586

Breast cancer stands as the foremost cause of cancer-related deaths among women worldwide. The prompt and accurate detection of breast lesions through ultrasound videos plays a crucial role in early diagnosis. However, existing ultrasound video lesion detectors often rely on multiple adjacent frames or non-local temporal fusion strategies to enhance performance, consequently compromising their detection speed. This study presents a simple yet effective network called the Space time Feature Aggregation Network (STA-Net). Its main purpose is to efficiently identify lesions in ultrasound videos. By leveraging a temporally shift-based space-time aggregation module, STA-Net achieves impressive real-time processing speeds of 54 frames per second on a single GeForce RTX 3090 GPU. Furthermore, it maintains a remarkable accuracy level of 38.7 mean average precision (mAP). Through extensive experimentation on the BUV dataset, our network surpasses existing state-of-the-art methods both quantitatively and qualitatively. These promising results solidify the effectiveness and superiority of our proposed STA-Net in ultrasound video lesion detection.

关键词： Ultrasound video Breast Lesion Detection

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Methods of increasing the contrast of images in video information systems 3

Methods of increasing the contrast of images in video inform...

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3rd International conference on Optics, Computer Applications, and Materials Science, CMSD 2023

作者： Beknazarova, S.S. Abdullayev, S.X. Abdullayeva, O.S. Abdullayev, Z.S. Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi 105 A. Temur Tashkent100142 Uzbekistan Namangan State University Namangan Uzbekistan Namangan Engeneering-Construction Institute Namangan Uzbekistan

ISBN: (纸本)9781510674486

In the article describe the methods of increasing the contrast of images in video information systems, the main compression of the video stream is provided by eliminating inter-frame redundancy using motion compensation methods for image fragments of adjacent frames. However, the use of motion compensation methods requires the formation of additional data (metadata) containing information about the types of image blocks used, the coordinates of their movement, etc. At the same time, in order to increase the compression of the video stream without compromising its quality, a higher accuracy of motion compensation is required, which leads to an increase in the number of blocks and, accordingly, to an increase in the volume of metadata that reduces the effectiveness of motion compensation. This is the main problem of compressing streaming video without degrading the quality of images. In addition, the higher accuracy of positioning blocks with motion compensation dramatically reduces the speed of image processing, which is not always feasible in real-time systems. © 2024 SPIE. All rights reserved.

关键词： Information systems

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Smart Monitoring and Guidance System for Parking Spaces 5

Smart Monitoring and Guidance System for Parking Spaces

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5th IEEE International conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Manvanth, G.S. Pavan Kumar, A. Priyanka, Valipe Devi Goduguluri, Varshitha Ravish, Roopa Pes University Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350383522

A parking system refers to the infrastructure and technology used to manage and facilitate the parking of vehicles in a specific area. These systems are designed to streamline the process of finding parking spaces, enhance efficiency, and improve overall traffic management. The proposed system is designed to address the challenges faced by open space parking spaces in major metropolitan cities. The prevailing issue of locating parking spaces in busy urban settings is tackled using Machine Learning (ML) model. Smart Monitoring and Guidance system is a ML based technology which allows the users to find vacant parking slots in a given area. The system uses ML model over Internet of Things (IoT) based solutions as scaling and maintenance is easier using ML models. The system provides real time information to the users about the total number of parking slots available at a parking space using image processing. The users can select a parking space of their choice. Upon selecting a suitable parking space, users are provided with real-time navigation assistance to the parking space of their choice. ML model improves the overall efficiency, reliability, and robustness of the system. The system obtains parking information of multiple parking spaces by processing their video footages concurrently. The system provides users with real-time navigation to the parking space thus providing the users and hassle free experience. © 2024 IEEE.

关键词： Directions assistance image processing Machine Learning Parking issues Parking Monitoring Parking slot availability real-time navigation real-time processing Smart city solutions Support Vector Classifier time-saving Traffic reduction

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Generalized self-cueing real-time attention scheduling with intermittent inspection and image resizing

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real-time SYSTEMS 2023年第2期59卷 302-343页

作者： Liu, Shengzhong Fu, Xinzhe Hu, Yigong Wigness, Maggie David, Philip Yao, Shuochao Sha, Lui Abdelzaher, Tarek Univ Illinois Champaign IL 61820 USA MIT Cambridge MA USA US DEVCOM Army Res Lab Adelphi NY USA George Mason Univ Fairfax VA USA

This paper proposes a generalized self-cueing real-time attention scheduling framework for DNN-based visual machine perception pipelines on resource-limited embedded platforms. Self-cueing means we identify subframe-level regions of interest in a scene internally by exploiting temporal correlations among successive video frames as opposed to externally via a cueing sensor. One limitation of our original self-cueing-and-inspection strategy (Liu et al. in Proceedings of the 28th IEEE real-time and embedded technology and applications symposium (RTAS), 2022b) lies in its lack of computational efficiency under high workloads, like busy traffic scenarios where a large number of objects are identified and separately inspected. We extend the conference publication by integrating image resizing with intermittent inspection and task batching in attention scheduling. The extension enhances the original algorithm by accelerating the processing of large objects by reducing their resolution at the cost of only a negligible degradation in accuracy, thereby achieving a higher overall object inspection throughput. After extracting partial regions around objects of interest, using an optical flow-based tracking algorithm, we allocate computation resources (i.e. DNN inspection) to them in a criticality-aware manner using a generalized batched proportional balancing algorithm (GBPB), to minimize a concept of generalized system uncertainty. It saves computational resources by inspecting low-priority regions intermittently at low frequencies and inspecting large objects at low resolutions. We implement the system on an NVIDIA Jetson Xavier platform and extensively evaluate its performance using a real-world driving dataset from Waymo. The proposed GBPB algorithm consistently outperforms the previous BPB algorithm that only uses intermittent inspection and a set of baselines. The performance gain of GBPB is larger in facing more significant resource constraints (i.e., lower sampling inte

关键词： real-time scheduling Object detection Temporal correlations Cyber-physical systems

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Reconnoitering the Essentials of image and video processing: A Comprehensive Overview

Reconnoitering the Essentials of Image and Video Processing:...

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2023 International conference on IoT, Communication and Automation Technology, ICICAT 2023

作者： Gupta, Ashulekha Sayal, Anu Chaithra, N. Jha, Janhvi Gupta, Veethika Maheshwari, Himani Department of Management Studies Uttarakhand Dehradun India School of Accounting and Finance Taylor's Business School Taylor's University Malaysia Department of Mathematics Bangalore India School of Social Sciences Doon University Department of Economics Uttarakhand Dehradun India Graphic Era Hill University Department of Computing Uttarakhand Dehradun India

ISBN: (纸本)9798350302820

Recent years have shown immense potential for image and video processing. Several fields in health, engineering, and science have used them to enhance functioning. In image processing, a number of algorithms and techniques are used to receive inputs in the form of images, such as pictures or video frames, and then produce an output which can either be a photo or a parameter that can be adjusted to enhance the image. When a video is processed, each frame is submitted to operations individually. A frame is a snapshot of the video at a certain point in time. Multiple frames may be taken in one second. Similar to photos, frames may be compared and contrasted based on their similarities and differences. To identify asteroids that could pose a threat and events like supernovae that are temporary, we need to use image processing techniques that work in real-time. Digital images are often compressed using a widely used method called the Discrete Cosine Transform (DCT). By using this technique, you can considerably decrease the size of your file without compromising the quality of the image that is visible to the human eye. Throughout this paper, we have examined several aspects of image and video processing and how they are used in scientific and technical fields. © 2023 IEEE.

关键词： image enhancement

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