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

共 8701 条记录，以下是231-240 订阅

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The Background Also Matters: Background-Aware Motion-Guided Objects Discovery

The Background Also Matters: Background-Aware Motion-Guided ...

引用

IEEE/CVF Winter conference on Applications of Computer Vision (WACV)

作者： Kara, Sandra Ammar, Hejer Chabot, Florian Quoc-Cuong Pham Univ Paris Saclay CEA List F-91120 Palaiseau France

ISBN: (纸本)9798350318920;9798350318937

Recent works have shown that objects discovery can largely benefit from the inherent motion information in video data. However, these methods lack a proper background processing, resulting in an over-segmentation of the non-object regions into random segments. This is a critical limitation given the unsupervised setting, where object segments and noise are not distinguishable. To address this limitation we propose BMOD, a Background-aware Motion-guided Objects Discovery method. Concretely, we leverage masks of moving objects extracted from optical flow and design a learning mechanism to extend them to the true foreground composed of both moving and static objects. The background, a complementary concept of the learned foreground class, is then isolated in the object discovery process. This enables a joint learning of the objects discovery task and the object/non-object separation. The conducted experiments on synthetic and real-world datasets show that integrating our background handling with various cutting-edge methods brings each time a considerable improvement. Specifically, we improve the objects discovery performance with a large margin, while establishing a strong baseline for object/non-object separation.

关键词： Algorithms Algorithms Algorithms and algorithms formulations image recognition and understanding Machine learning architectures video recognition and understanding

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Lane Detection for Autonomous vehicles using image Transformation Techniques 5

Lane Detection for Autonomous vehicles using Image Transform...

引用

5th IEEE International conference for Emerging Technology, INCET 2024

作者： Shettar, Poonam M. Jadhav, Abhijeet Karoshi, Divya Aishwarya, G. Kulkarni, Akash SoECE KLE Technological University Hubballi India

ISBN: (纸本)9798350361155

Lane detection is critical in autonomous driving and advanced driver assistance systems (ADAS), furnishing vital information for vehicle navigation and safety. The study introduces lane detection methodology leveraging established image processing techniques within the image transformation frameworks, including the study of kernels. This approach accurately detects lane markings in real-time images or video streams with the help of Gaussian blur and Canny edge detection. The system's evaluation primarily focuses on structured roads under standard conditions, demonstrating its efficacy in such environments. However, the potential for enhancing vehicle autonomy and safety across varied driving scenarios remains prominent. Leveraging image transformation capabilities and the insights gained from kernel studies, this research advances computer vision applications in the automotive sector, facilitating the evolution of more intelligent and adaptable driving ***, the study introduces a method for assessing the accuracy of the detected lanes by calculating the intersection over union (IOU). © 2024 IEEE.

关键词： Autonomous vehicles

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End-to-End video Snapshot Compressive Imaging using video Transformers 11

End-to-End Video Snapshot Compressive Imaging using Video Tr...

引用

11th International conference on image processing Theory, Tools and Applications (IPTA)

作者： Saideni, Wael Courreges, Fabien Helbert, David Cances, Jean Pierre XLIM Res Inst CNRS UMR 7252 Limoges France XLIM Res Inst CNRS UMR 7252 Brive La Gaillarde France XLIM Res Inst CNRS UMR 7252 Poitiers France

ISBN: (纸本)9781665469647

This paper presents a novel reconstruction algorithm for video Snapshot Compressive Imaging (SCI). Inspired by recent research works on Transformers and Self-Attention mechanism in computer vision, we propose the first video SCI reconstruction algorithm built upon Transformers to capture long-range spatio-temporal dependencies enabling the deep learning of feature maps. Our approach is based on a Spatiotemporal Convolutional Multi-head Attention (ST-ConvMHA) which enable to exploit the spatial and temporal information of the video scenes instead of using fully-connected attention layers. To evaluate the performances of our approach, we train our algorithm on DAVIS2017 dataset and we test the trained models on six benchmark datasets. The obtained results in terms of PSNR, SSIM and especially reconstruction time prove the ability of using our reconstruction approach for real-time applications. We truly believe that our research will motivate future works for more video reconstruction approaches.

关键词： Vision Transformers deep learning optimization computer vision video compressive sensing

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Blind lane recognition based on context salient attention mechanism and transfer learning 5

Blind lane recognition based on context salient attention me...

引用

5th International conference on image, video processing, and Artificial Intelligence, IVPAI 2023

作者： Li, Lin School of Computer Science Chengdu Normal University No.99 Haike Road Chengdu611130 China

ISBN: (纸本)9781510674660

It’s very helpful for the person with visual impairments to assist in detecting blind lane, A novelty real-time detection for blind lane detection based on context salient attention mechanism and transfer learning is proposed. The method is based on transfer learning and feature integration with the visual attention mechanism. Firstly, the bottleneck descriptors and salient attention-based features are extracted through transformer-like feature integration, and then these features are incorporated together. Secondly, we train the new model based on a pre-trained model with careful parameter tuning. In the experiments, samples of blind lane images are collected at different areas in Chengdu for training and validation with different model cases. The experimental results show that the proposed method has advantages in classification precision by directly using Swin transformer by transfer learning with an attention mechanism. Our method is better than the original one, and the total accuracy is increased from 93.98% to 96.03% on the standard flower photos dataset. We got a total precision of 99% on our own blind road datasets. © 2024 SPIE. All rights reserved.

关键词： image classification

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A Vibe algorithm based on fused edge information optimization 3

A Vibe algorithm based on fused edge information optimizatio...

引用

3rd International conference on Signal image processing and Communication, ICSIPC 2023

作者： Yang, Tianqi Wei, Wei Chen, Yuxuan Sun, Kai College of Electronic and Information Engineering Inner Mongolia University Hohhot China

ISBN: (纸本)9781510670945

Foreground detection is a significant area of study within the realm of computer vision and plays a crucial role in video-based applications. The Vibe algorithm is an efficient foreground detection method, and this paper proposes an optimized Vibe algorithm that incorporates fused edge information to address the issues of "ghosting" and noise. Initially, the edge information derived from the three-frame method is enhanced by the Canny operator. Subsequently, the OTSU algorithm is employed to filter out additional noise in the image, thereby enhancing the robustness of the algorithm. Finally, the optimized three-frame difference method is fused with the Vibe algorithm to eliminate the "ghosting" and achieve a complete foreground target. Experimental results demonstrate that the proposed algorithm effectively removes "ghosting" and noise, providing good real-time performance and robustness, thereby meeting the application requirements of real-time detection. © 2023 SPIE.

关键词： image enhancement

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KNOWLEDGE DISTILLATION FOR MULTI-TARGET DOMAIN ADAPTATION IN real-time PERSON RE-IDENTIFICATION 29

KNOWLEDGE DISTILLATION FOR MULTI-TARGET DOMAIN ADAPTATION IN...

引用

IEEE International conference on image processing (ICIP)

作者： Remigereau, Felix Mekhazni, Djebril Abdoli, Sajjad Nguyen, Le Thanh ETS Lab dImagerie Vis & dIntelligence Artificielle LI Dept Syst Engn Montreal PQ Canada

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Despite the recent success of deep learning architectures, person re-identification (ReID) remains a challenging problem in real-word applications. Several unsupervised single-target domain adaptation (STDA) methods have recently been proposed to limit the decline in ReID accuracy caused by the domain shift that typically occurs between source and target video data. Given the multimodal nature of person ReID data (due to variations across camera viewpoints and capture conditions), training a common CNN backbone to address domain shifts across multiple target domains, can provide an efficient solution for real-time ReID applications. Although multi-target domain adaptation (MTDA) has not been widely addressed in the ReID literature, a straightforward approach consists in blending different target datasets, and performing STDA on the mixture to train a common CNN. However, this approach may lead to poor generalization, especially when blending a growing number of distinct target domains to train a smaller CNN. To alleviate this problem, we introduce a new MTDA method based on knowledge distillation (KD-ReID) that is suitable for real-time person ReID applications. Our method adapts a common lightweight student backbone CNN over the target domains by alternatively distilling from multiple specialized teacher CNNs, each one adapted on data from a specific target domain. Extensive experiments(1) conducted on several challenging person ReID datasets indicate that our approach outperforms state-of-art methods for MTDA, including blending methods, particularly when training a compact CNN backbone like OSNet. Results suggest that our flexible MTDA approach can be employed to design cost-effective ReID systems for real-time video surveillance applications.

关键词： video Surveillance Person Re-Identification Unsupervised Domain Adaptation Knowledge Distillation

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Bitrate Control for VVC based on RD-Cost Estimation with YOLOv8 13

Bitrate Control for VVC based on RD-Cost Estimation with YOL...

引用

13th IEEE Global conference on Consumer Electronic, GCCE 2024

作者： Goto, Kaito Katayama, Takafumi Song, Tian Shimamoto, Takashi Tokushima University Department of Science and Engineering Minami-Josanjima 2-1 Tokushima City770-8506 Japan Tokushima University Institute of Technology and Science Graduate School of Engineering Minami-Josanjima 2-1 Tokushima City770-8506 Japan

ISBN: (纸本)9798350355079

In recent applications, a modern object recognition model is available together with the video encoder. In this work, an adaptive bitrate control algorithm is proposed using a You Only Look Once v8 (YOLOv8) model for Versatile video Coding (VVC). YOLOv8 object detection network, which is also suitable for real-time processing, is used as an algorithm to detect salient regions before encoding. The proposed algorithm estimates Rate-Distortion Cost (RD-Cost) of Coding Tree Units (CTUs) with YOLOv8 and selects the Quantization Parameter (QP) based on the object detection result. Simulation results show that the proposed algorithm achieves a maximum bitrate reduction rate of 7.76% and improves coding efficiency while minimizing image quality degradation. © 2024 IEEE.

关键词： image enhancement

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COVER: A Comprehensive video Quality Evaluator

COVER: A Comprehensive Video Quality Evaluator

引用

IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： He, Chenlong Zheng, Qi Zhu, Ruoxi Zeng, Xiaoyang Fan, Yibo Tu, Zhengzhong Fudan Univ Shanghai Peoples R China Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9798350365474

video quality assessment, especially for a massive scale of user-generated content, is an essential yet challenging computer vision and video analysis problem. Prior methods have been shown to be effective in mirroring subjective human opinion scores;however, they fail to capture the complicated, multi-dimensional aspects of factors that impact the overall perceptual quality. In this paper, we introduce COVER, a comprehensive video quality evaluator, a novel framework designed to evaluate video quality holistically - from a technical, aesthetic, and semantic perspective. Specifically, COVER leverages three parallel branches: (1) a Swin Transformer backbone implemented on spatially sampled crops to predict technical quality;(2) a ConvNet employed on subsampled frames to derive aesthetic quality;(3) a CLIP image encoder executed on resized frames to obtain semantic quality. We further propose a simplified cross-gating block to interact with the three branches before feeding into the predicting head. The final quality score is attained using a weighted sum of each sub-score, making a multi-faceted metric. Our experimental results demonstrate that COVER exceeds the state-of-the-art models in multiple UGC video quality datasets. Moreover, COVER offers a diagnosable quality report to explain the quality score in multiple pillars, while it is capable of processing 1080p videos at 3x faster speed than the real-time requirement. To facilitate future research on efficient and explainable video quality research, the code is available at https://***/vztu/COVER.

关键词： User-generated Content video Quality Assessment

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real-time Detection of Illegally Parked Vehicles in Roadside Parking Areas Based on Intelligent video Terminals 24

Real-time Detection of Illegally Parked Vehicles in Roadside...

引用

2024 International conference on image processing, Intelligent Control and Computer Engineering, IPICE 2024

作者： Tang, Kang Sun, Yu Zhong, Xiaoyang Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou University China

ISBN: (纸本)9798400710285

With the development of artificial intelligence technology, urban traffic management has become increasingly convenient, and the task of illegal parking detection has become a major research focus. Currently, most illegal parking detection schemes use fixed-point cameras, which not only waste resources but also have the limitation of a small detection range. To overcome this issue, we have designed a highly mobile system for detecting illegally parked vehicles. Considering the different relative positions of the tires and parking lines in roadside parking areas, we use an optimized You Only Look Once(YOLOv5) algorithm to classify the tires and determine whether the vehicle is illegally parked based on different category of tires combinations. Subsequently, we applied the above-mentioned illegal parking detection strategy to a embedded device which mounted on the mobile vehicle to realize the real-time mobile detection and information collection of illegal vehicles in the roadside parking area. © 2024 Copyright held by the owner/author(s).

关键词： Tires

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Research on a Parallel Algorithm for video image Compression of Transmission Line Inspection

Research on a Parallel Algorithm for Video Image Compression...

引用

2023 International conference on Big Data Mining and Information processing, BDMIP 2023

作者： Hu, Meihui Li, Kai Wan, Jiao Chen, Tao Xiang, Zhiwei State Grid Xinjiang Information & Telecommunication Company Xinjiang Urumqi830017 China State Grid Xinjiang Electric Power Co. Ltd. Xinjiang Urumqi830017 China

ISBN: (纸本)9798400709166

Unmanned aerial vehicle (UAV) has the advantages of simple operation, sensitive response, flexible flight, long battery life and low cost, and has become a conventional way of power inspection. However, the video signal with huge data will bring a certain burden to the hardware of the data acquisition end of the system, so it is necessary to improve the sampling performance of the data acquisition end of the video compression system. In this paper, a parallel algorithm for video image compression of power transmission line inspection is proposed. By using the message passing interface function provided by MPI (message passing interface), the search and matching process of image domain block and value domain block is distributed to multiple processors for simultaneous execution. The experimental results show that when only one computing node is used, the CPU utilization efficiency is very close when the images with the same compression ratio are decompressed in two parallel modes. With the increase of the number of computing nodes, the efficiency of MPI parallel mode decreases gradually, while the efficiency of MPI+Open MP hybrid model increases. This study has certain reference value and practical value for real-time processing of transmission line inspection data. © 2023 ACM.

关键词： image compression

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