This study investigates the different aspects of multimedia computing in video Synthetic Aperture Radar(video-SAR)as a new mode of radar imaging for real-time remote sensing and *** research also considers new suggest...
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This study investigates the different aspects of multimedia computing in video Synthetic Aperture Radar(video-SAR)as a new mode of radar imaging for real-time remote sensing and *** research also considers new suggestions in the systematic design,research taxonomy,and future trends of radar data *** the conventional modes of SAR imaging,video-SAR can generate video sequences to obtain online monitoring and green surveillance throughout the day and night(regardless of light sources)in all ***,an introduction to video-SAR is ***,some specific properties of this imaging mode are ***,this research covers one of the most important aspects of the video-SAR systems,namely,the systematic design requirements,and also some new types of visual distortions which are different from the distortions,artifacts and noises observed in the conventional imaging *** addition,some topics on the general features and high-performance computing of video-SAR towards radar communications through Unmanned Aerial Vehicle(UAV)platforms,Internet of Multimedia Things(IoMT),video-SAR data processing issues,and real-world applications are investigated.
Due to the high imaging resolution and large detection area, side-scan sonar (SSS) has a wide range of applications in underwater detection, such as shipwreck and wrecked aircraft search and rescue. However, limited b...
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Due to the high imaging resolution and large detection area, side-scan sonar (SSS) has a wide range of applications in underwater detection, such as shipwreck and wrecked aircraft search and rescue. However, limited by complex underwater environments and insufficient detection equipment performance, it is still very difficult to achieve real-time automatic object detection during the search and rescue process. To solve these problems, a new lightweight SSS image object detection model, GCT-YOLOv5, is proposed in this paper. Firstly, the C3Ghost and Ghost module are integrated into the proposed model to reduce the computational complexity. Subsequently, the coordinate attention mechanism is embedded within the backbone network to extract crucial features of objects and suppress the interference of background noise, thereby enhancing detection accuracy. Finally, transposed convolution is adopted in the neck network to achieve higher up-sampling performance and further enhance the ability of object feature perception. The experimental results demonstrate that compared to YOLOv5s, GCT-YOLOv5 has 48% fewer number of parameters and 45% fewer FLOPs. Furthermore, mAP@0.5 and mAP@0.5:0.95 of GCT-YOLOv5 are raised by 3.1 and 1.5%, respectively. GCT-YOLOv5 outperforms various commonly used object detection algorithms of SSS image in terms of accuracy and speed. In general, the GCT-YOLOv5 has the characteristics of strong robustness, lightweight, and high efficiency, which is especially suitable for situations requiring real-time detection based on SSS image. The source code and dataset of GCT-YOLOv5 can be viewed at https://***/gx-123/GCT-YOLOv5.
With the increase of time, the problem of image quality reduction caused by unbalanced network load will appear in the real-time broadcasting of sports events. A real-time broadcasting method of sports events is desig...
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ISBN:
(数字)9783031288678
ISBN:
(纸本)9783031288661;9783031288678
With the increase of time, the problem of image quality reduction caused by unbalanced network load will appear in the real-time broadcasting of sports events. A real-time broadcasting method of sports events is designed by using wireless network communication technology. Audio and video decoding and coding are divided into two independent threads working at the same time, which can make the frame rate reach the HD standard and enhance the stability of the encoding and decoding process. The GAN model is used to enhance the rate conversion. In the inter frame mode, integer transformation, quantization, reordering and entropy coding are performed on the residual block to complete the coding of the macroblock, which is stored or transmitted through the NAL layer. Wireless network communication technology is applied to distribute the number of channels in the space of mutual interference and balance the load of relay network. For the viewer, after receiving the streaming media data block, analyze the RTP packet, decode the video data, and then play the video. The test results showthat the real-time broadcastingmethod of sports events usingwireless network communication technology can improve PSNR, reduce the distortion of video sequence and ensure the stability of output picture.
To ensure stable operation of the power distribution equipment, it is necessary to monitor the operational status of the equipment in realtime. video surveillance, which records image information from distribution sc...
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This paper deals with the problem of video-based face recognition. Nowadays, facial recognition methods have made a big step forward, but video-based recognition with its poor quality, difficult lighting conditions, a...
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This paper deals with the problem of video-based face recognition. Nowadays, facial recognition methods have made a big step forward, but video-based recognition with its poor quality, difficult lighting conditions, and real-time requirements is still a difficult and unfinished *** paper uses the apparatus of convolutional networks for various stages of processing: for capturing and detecting a face, for constructing a feature vector, and finally for recognition. All algorithms are implemented and studied in the Matlab environment to simplify their further export to embedded applications.
The Scalable High Efficiency video Coding (SHVC) standard is a state-of-the-art technology for scalable video coding that uses an exhaustive prediction mode decision algorithm within a recursive quad-tree structure co...
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ISBN:
(纸本)9798350351491;9798350351484
The Scalable High Efficiency video Coding (SHVC) standard is a state-of-the-art technology for scalable video coding that uses an exhaustive prediction mode decision algorithm within a recursive quad-tree structure coding unit to enhance coding efficiency. However, this improvement comes at the cost of increased computational complexity during encoding, which poses a challenge. To address this challenge, a novel approach has been proposed to accelerate the SHVC encoder. The proposed technique leverages the inherent spatial correlation between the selected Prediction Unit (PU) within the enhancement layer (EL) and its corresponding PU in the base layer (BL). This strategic exploitation yields impressive results, as evidenced by significant reductions in encoding time. It is worth noting that this efficiency boost is achieved with a manageable compromise in video quality and an acceptable uptick in bit rate, which remains well within the bounds of mainstream media requirements. In practice, the proposed methodology showcases its prowess by consistently achieving an average time-saving of 81.77% and 81.59%, thereby underscoring its effectiveness in practical scenarios.
Object detection plays a vital role in the video surveillance *** enhance security,surveillance cameras are now installed in public areas such as traffic signals,roadways,retail malls,train stations,and ***,monitor-ing...
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Object detection plays a vital role in the video surveillance *** enhance security,surveillance cameras are now installed in public areas such as traffic signals,roadways,retail malls,train stations,and ***,monitor-ing the video continually at a quicker pace is a challenging *** a consequence,security cameras are useless and need human *** primary difficulty with video surveillance is identifying abnormalities such as thefts,accidents,crimes,or other unlawful *** anomalous action does not occur at a high-er rate than usual *** detect the object in a video,first we analyze the images pixel by *** digital imageprocessing,segmentation is the process of segregating the individual image parts into *** performance of segmenta-tion is affected by irregular illumination and/or low *** factors highly affect the real-time object detection process in the video surveillance *** this paper,a modified ResNet model(M-Resnet)is proposed to enhance the image which is affected by insufficient *** results provide the comparison of existing method output and modification architecture of the ResNet model shows the considerable amount improvement in detection objects in the video *** proposed model shows better results in the metrics like preci-sion,recall,pixel accuracy,etc.,andfinds a reasonable improvement in the object detection.
ObjectiveRoad traffic accidents have become a serious social problem, with a significant proportion of accidents caused by insufficient visibility on roads at night. Therefore, nighttime road visibility detection base...
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ObjectiveRoad traffic accidents have become a serious social problem, with a significant proportion of accidents caused by insufficient visibility on roads at night. Therefore, nighttime road visibility detection based on videoimages has become one of the difficulties and a key issue in domestic and international *** study analyzes the importance of nighttime road visibility monitoring, introduces the structure, working principle, and monitoring method of a videoimage nighttime visibility monitoring system, and proposes a nighttime road visibility monitoring method based on videoimages. Based on the characteristics of nighttimeimages, an improved dark channel prior method was adopted to calculate the nighttime road visibility. This method mainly includes eight steps: videoimage acquisition, image grayscale processing, calculation of image average variance, image average gradient, drawing grayscale histograms, image enhancement based on the calculated values, calculation of transmittance, and calculation of *** experimental results show that the proposed night road visibility monitoring method based on videoimages can effectively realize real-time monitoring of night road visibility, effectively overcome the inherent defects of traditional methods, and the constructed night visibility monitoring framework can realize high-precision visibility calculation, and has broad application *** adaptive threshold and adaptive filtering technology, the improved dark channel algorithm has shown competitive advantages in both image quality index and practical application effect, especially in noise suppression and edge preservation. However, under extreme illumination conditions, the algorithm still has room for improvement in the processing of the strong light source region, and the dark channel prior may lead to bias in the transmission estimation.
The accuracy and real-time performance of existing traffic sign recognition methods in complex environments need to be improved. This study aims to propose an efficient traffic sign recognition solution based on machi...
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We study a multi-task decision-making problem for 360. videoprocessing in a wireless multi-user virtual reality (VR) system that includes an edge computing unit (ECU) to deliver 360. videos to VR users and offer comp...
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ISBN:
(纸本)9798350351439;9798350351422
We study a multi-task decision-making problem for 360. videoprocessing in a wireless multi-user virtual reality (VR) system that includes an edge computing unit (ECU) to deliver 360. videos to VR users and offer computing assistance for decoding/rendering of video frames. However, this comes at the expense of increased data volume and required bandwidth. To balance this trade-off, we formulate a constrained quality of experience (QoE) maximization problem in which the rebuffering time and quality variation between video frames are bounded by user and video requirements. To solve the formulated multi-user QoE maximization, we leverage deep reinforcement learning (DRL) for multi-task rate adaptation and computation distribution (MTRC). The proposed MTRC approach does not rely on any predefined assumption about the environment and relies on video playback statistics (i.e., past throughput, decoding time, transmission time, etc.), video information, and the resulting performance to adjust the video bitrate and computation distribution. We train MTRC with real-world wireless network traces and 360. video datasets to obtain evaluation results in terms of the average QoE, peak signal-to-noise ratio (PSNR), rebuffering time, and quality variation. Our results indicate that the MTRC improves the users' QoE compared to state-of-the-art rate adaptation algorithm. Specifically, we show a 5.97 dB to 6.44 dB improvement in PSNR, a 1.66X to 4.23X improvement in rebuffering time, and a 4.21 dB to 4.35 dB improvement in quality variation.
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