The video platforms that users watch leak the privacy of their preferences. More and more video streaming is being encrypted to protect users' privacy. In addition, many users use VPN to enhance their privacy prot...
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The video platforms that users watch leak the privacy of their preferences. More and more video streaming is being encrypted to protect users' privacy. In addition, many users use VPN to enhance their privacy protection further. VPN makes video platform identification challenging because it poses traffic obfuscation and further data encryption. Although the segment-based transmission mechanism and Variable BitRate encoding in HAS make network video traffic show still identifiable patterns, most existing work cannot distinguish different platforms due to the similarity of video streaming. Therefore, we propose a traffic-based side-channel attack method to identify VPN video streaming platforms in realtime. The aggregated feature sequence of the unidirectional video streaming is extracted to significantly retain the characteristics of different video platforms. Experiments on 10Gbps backbone background traffic show that the F1-score of the method exceeds 97% and can be processed in realtime. In addition, we verify the method's robustness on datasets with different path features and encryption techniques. A comparison with similar methods shows that our method only requires 1/1260 of the storage and 1/60 of the processingtime to identify accurately.
YOLO has developed into a primary real-time object identification platform for applications such as video surveillance systems, autonomous vehicles, and robots. This research proposes an improved real-time object reco...
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In the past few years, industrial software has entered a relatively mature stage in terms of product technology development and function growth mode, so the functions are more convergent, and the adopted technology ca...
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In the current video-based Dynamic Mesh Coding (V-DMC) standard, inter-frame coding is restricted to mesh frames with constant topology. Consequently, temporal redundancy is not fully leveraged, resulting in suboptima...
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ISBN:
(纸本)9798331529543;9798331529550
In the current video-based Dynamic Mesh Coding (V-DMC) standard, inter-frame coding is restricted to mesh frames with constant topology. Consequently, temporal redundancy is not fully leveraged, resulting in suboptimal compression efficacy. To address this limitation, this paper introduces a novel coarse-to-fine scheme to generate anchor meshes for frames with time-varying topology. Initially, we generate a coarse anchor mesh using an octree-based nearest neighbor search. Motion estimation compensates for regions with significant motion changes during this process. However, the quality of the coarse mesh is low due to its suboptimal vertices. To enhance details, the fine anchor mesh is further optimized using the Quadric Error Metrics (QEM) algorithm to calculate more precise anchor points. The inter-frame anchor mesh generated herein retains the connectivity of the reference base mesh, while concurrently preserving superior quality. Experimental results show that our method achieves 7.2% similar to 10.3% BD-rate gain compared to the existing V-DMC test model version 7.
video object detection aims to identify the objects that appear in the frame of video *** challenges in video object detection include the degradation of video frames caused by fast motion,occlusion,scale variations,a...
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We present MobileSky, the first automatic method for real-time high-quality sky replacement for mobile AR applications. The primary challenge of this task is how to extract sky regions in camera feed both quickly and ...
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We present MobileSky, the first automatic method for real-time high-quality sky replacement for mobile AR applications. The primary challenge of this task is how to extract sky regions in camera feed both quickly and accurately. While the problem of sky replacement is not new, previous methods mainly concern extraction quality rather than efficiency, limiting their application to our task. We aim to provide higher quality, both spatially and temporally consistent sky mask maps for all camera frames in realtime. To this end, we develop a novel framework that combines a new deep semantic network called FSNet with novel post-processing refinement steps. By leveraging IMU data, we also propose new sky-aware constraints such as temporal consistency, position consistency, and color consistency to help refine the weakly classified part of the segmentation output. Experiments show that our method achieves an average of around 30 FPS on off-the-shelf smartphones and outperforms the state-of-the-art sky replacement methods in terms of execution speed and quality. In the meantime, our mask maps appear to be visually more stable across frames. Our fast sky replacement method enables several applications, such as AR advertising, art making, generating fantasy celestial objects, visually learning about weather phenomena, and advanced video-based visual effects. To facilitate future research, we also create a new video dataset containing annotated sky regions with IMU data.
作者：
Kim, YuraKim, Yong-Hwan
Intelligent Image Processing Research Center Seongnam-si Korea Republic of
In 2021, the MPEG introduced the video-based point cloud compression(V-PCC) standard, achieving an excellent 3D point cloud data compression ratio. However, the high computational complexity made real-time encoding im...
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The extraction of adherent cells, such as B16 murine melanoma cells, from Petri dish cultures is critical in biomedical applications, including cell reprogramming, transplantation, and regenerative medicine. Tradition...
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The extraction of adherent cells, such as B16 murine melanoma cells, from Petri dish cultures is critical in biomedical applications, including cell reprogramming, transplantation, and regenerative medicine. Traditional detachment methods-enzymatic, mechanical, or chemical-often compromise cell viability by altering membrane integrity and disrupting adhesion proteins. To address these challenges, this study investigated sensor-based detection of the pre-detachment phase in a MPWM (Microfluidic Pulse Width Modulation) extraction system. Our approach integrates a micromechatronic system with a microfluidic suction circuit, real-time CCD imaging, and computational analysis to detect and characterize the pre-detachment moment before full extraction. A precisely controlled hydrodynamic force field progressively disrupts adhesion in multiple stages, reducing mechanical stress and preserving cell integrity. real-timevideo analysis enables continuous monitoring of positional dynamics and oscillatory responses. imageprocessing and deep learning algorithms determine object center coordinates, allowing the MPWM system to dynamically adjust suction parameters. This optimizes detachment while minimizing liquid absorption and reflux volume, ensuring efficient extraction. By combining microfluidics, sensor detection, and AI-driven imageprocessing, this study established a non-invasive method for optimizing adherent cell detachment. These findings have significant implications for single-cell research, regenerative medicine, and high-throughput biotechnology, ensuring maximal viability and minimal perturbation.
In this study, we implemented an uncompressed 8K-video frame conversion software to evaluate the availability of Message Passing Interface (MPI) for real-timeprocessing. We measured the processingtime using an actua...
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Nowadays, with the rapid development of Internet technology and the emergence of converged media, the rapid transmission of news and information and the ways of transmission are increasingly diversified, and the trans...
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