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Evaluation Of Bitrate Ladders For Versatile Video Coder

Evaluation Of Bitrate Ladders For Versatile Video Coder

引用

IEEE visual communications and image processing (VCIP)

作者： Reda Kaafarani Mederic Blestel Thomas Maugey Michael Ropert Aline Roumy Mediakind France Rennes Inria Rennes France

ISBN: (纸本)9781728173221

Many video service providers take advantage of bitrate ladders in adaptive HTTP video streaming to account for different network states and user display specifications by providing bitrate/resolution pairs that best fit client's network conditions and display capabilities. These bitrate ladders, however, differ when using different codecs and thus the couples bitrate/resolution differ as well. In addition, bitrate ladders are based on previously available codecs (H.264/MPEG4-AVC, HEVC, etc.), i.e. codecs that are already in service, hence the introduction of new codecs e.g. Versatile Video Coding (VVC) requires re-analyzing these ladders. For that matter, we will analyze the evolution of the bitrate ladder when using VVC. We show how VVC impacts this ladder when compared to HEVC and H.264/AVC and in particular, that there is no need to switch to lower resolutions at the lower bitrates defined in the Call for Evidence on Transcoding for Network Distributed Video Coding (CfE).

关键词： Performance evaluation Codecs image resolution image coding visual communication Bit rate Transcoding

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Discrimination of Haploid and Diploid Maize Seeds Based on Deep Features 28

Discrimination of Haploid and Diploid Maize Seeds Based on D...

引用

28th Signal processing and communications Applications conference (SIU)

作者： Donmez, Emrah Inonu Univ Muhendislik Fak Bilgisayar Muhendisligi Malatya Turkey

ISBN: (纸本)9781728172064

One of the main processes in in vivo maternal haploid breeding method which is widely used in hybrid maize breeding in recent years is the separation of haploid and diploid individuals. Although different approaches have been proposed to make this distinction, the R1-nj color marker is widely and successfully used. The R1-nj color marker allows the visual separation of haploid and diploid individuals during the seed period. Nowadays, this distinction is done manually, causing loss of time and labor as well as high error. In this study, an open access dataset consisting of 3,000 maize seed images was used. The deep features from the FC6, FC7 and FC8 fully connected layers of the AlexNet architecture are classified with the support vector machine. 10-fold cross-validation test was used to evaluate model performances. Experimental results showed that the best classification performance is possible with 89.50% accuracy using deep features obtained from the FC6 fully connected layer.

关键词： haploid maize seed identification image processing convolutional neural networks deep features artificial learning

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Securing Intelligent Vehicular Networks Using AI-Driven Federated Learning

Securing Intelligent Vehicular Networks Using AI-Driven Fede...

引用

International conference on Computer and communications Technologies (ICCCT)

作者： N Shivaanivarsha J Swetha VL Ram Lashmi G S Yaswanth Rao Department of Electronics and Communication Engineering Sri Sairam Engineering College Chennai India

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

Analysis of Detour Index in Graph Joins and Special Graph Structures Vehicular networks are crucial for modern transportation safety and efficiency. However, in adverse weather conditions such as fog, reduced visibility severely disrupts vehicle-to-vehicle (V2V) communication, increasing the risk of chain accidents. Traditional centralized approaches for managing vehicular communication suffer from high latency, limited scalability, and data privacy concerns, making them unsuitable for real-time decision-making in dynamic environments. To address these challenges, this project proposes a Federated Learning Framework for Securing Vehicular Networks Using AI, enabling decentralized, privacy-preserving, and real-time threat detection in vehicular networks. The proposed system leverages Artificial Intelligence (AI) and Federated Learning (FL) to enhance situational awareness and predict collision risks. A deep learning-based object detection model, utilizing the YOLO algorithm, identifies obstacles, vehicles, and hazardous conditions in low-visibility environments. Edge detection techniques further improve visual clarity and detection accuracy. Federated Learning enables vehicles to collaboratively train AI models on local data without sharing raw information, ensuring data privacy and adaptability to real-world scenarios. Secure vehicle-to-vehicle (V2V) communication enhances decision-making speed, reducing latency in critical situations. By integrating AI-driven predictive analysis, federated learning, and decentralized data processing, this system significantly improves vehicular safety in challenging weather conditions. It ensures real-time hazard detection, reduces the likelihood of chain accidents, enhances data privacy, and scales effectively for large vehicular networks. This innovation marks a critical step toward intelligent, autonomous, and resilient transportation systems in smart cities.

关键词： YOLO Data privacy Federated learning image edge detection Decision making Vehicular ad hoc networks Transportation Real-time systems Artificial intelligence Meteorology

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Quadrotor visual Navigation Under GPS Spoofing Attack

Quadrotor Visual Navigation Under GPS Spoofing Attack

引用

International conference on Wireless communications, Signal processing and Networking (WiSPNET)

作者： Kai Liu Yuyang Zhao Gang Wang Rui Xue School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electronics and Information Engineering Beihang University Beijing China

This paper presents a quadrotor visual navigation system under the GPS spoofing attack. We use HACKRF to send spoofing signals to the quadrotor. The quadrotor is detected during the outer loop of the system. If the quadrotor is spoofed, the quadrotor switch to visual navigation instead of using GPS signals. In the visual navigation, the image matching technology is used to solve the actual position of the quadrotor for the positioning problem of the quadrotor. A method of information fusion based on reference map matching and motion estimation is proposed for positioning and navigation. We can extraction keypoints, get the image descriptor based on ORB algorithm and binarize them with the corresponding correct geographical coordinates as a reference map. In order to improve the search matching speed, KLT keypoint tracking algorithm is used to estimate the motion information of the quadrotor, and the rough position of auxiliary search reference map is given. Simulations and experiments show the feasibility of the proposed scheme.

关键词： Wireless communication visualization Navigation Tracking image matching Software algorithms Signal processing algorithms

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Action Recognition Improved by Correlations and Attention of Subjects and Scene

Action Recognition Improved by Correlations and Attention of...

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IEEE visual communications and image processing (VCIP)

作者： Manh-Hung Ha Oscal Tzyh-Chiang Chen National Chung Cheng University Chiayi Taiwan

ISBN: (纸本)9781728173221

Comprehensive activity understanding of multiple subjects in a video requires subject detection, action identification, and behavior interpretation as well as the interactions among subjects and background. This work develops the action recognition of subject(s) based on the correlations and interactions of the whole scene and subject(s) by using the Deep Neural Network (DNN). The proposed DNN consists of 3D Convolutional Neural Network (CNN), Spatial Attention (SA) generation layer, mapping convolutional fused-depth layer, Transformer Encoder (TE), and two fully connected layers with late fusion for final classification. Especially, the attention mechanisms in SA and TE are implemented to find out meaningful action information on spatial and temporal domains for enhancing recognition performance, respectively. The experimental results reveal that the proposed DNN shows the superior accuracies of 97.8%, 98.4% and 85.6% in the datasets of traffic police, UCF101-24 and JHMDB-21, respectively. Therefore, our DNN is an outstanding classifier for various action recognitions involving one or multiple subjects.

关键词： Deep learning Three-dimensional displays Correlation image recognition visual communication Law enforcement Neural networks

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ConvLSTM-based Neural Network for Video Semantic Segmentation

ConvLSTM-based Neural Network for Video Semantic Segmentatio...

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IEEE visual communications and image processing (VCIP)

作者： Lan Zhou Hui Yuan Chuan Ge School of Contr. Sci. and Eng. Shandong University Jinan China China Mob. Gr. Shandong Co. Ltd. Jinan China

ISBN: (纸本)9781728173221

We propose a convolutional long short-term memory(ConvLSTM)-based neural network for video semantic segmentation. The network can capture the timing information between frames through the ConvLSTM module to improve the prediction accuracy. The back-bone network uses dense connection, atrous convolution, and pooling pyramid structure to expand the receptive field. During the training, to avoid over fitting, data augmentation and learning rate attenuation strategies were used. The proposed method is end-to-end trainable and evaluated on the street scene benchmark Cityscapes dataset. Experimental results show that, benefit from the ConvLSTM module, the proposed network can extract temporal information between frames effectively, and thus improve the accuracy of video semantic segmentation, especially for dynamic objects and small obiects. such as truck. pedestrian and pole.

关键词： Training image segmentation Convolution visual communication Semantics Neural networks Fitting

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Deep Metric Learning for Human Action Recognition with SlowFast Networks

Deep Metric Learning for Human Action Recognition with SlowF...

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IEEE visual communications and image processing (VCIP)

作者： Shanmeng Shi Cheolkon Jung School of Electronic Engineering Xidian University China

ISBN: (纸本)9781728173221

In this paper, we propose deep metric learning for human action recognition with SlowFast networks. We adopt SlowFast Networks to extract slow-changing spatial semantic information of a single target entity in the spatial domain with fast-changing motion information in the temporal domain. Since deep metric learning is able to learn the class difference between human actions, we utilize deep metric learning to learn a mapping from the original video to the compact features in the embedding space. The proposed network consists of three main parts: 1) two branches independently operating at low and high frame rates to extract spatial and temporal features; 2) feature fusion of the two branches; 3) joint training network of deep metric learning and classification loss. Experimental results on the KTH human action dataset demonstrate that the proposed method achieves faster runtime with less model size than C3D and R3D, while ensuring high accuracy.

关键词： Measurement Training Runtime image recognition visual communication Semantics Feature extraction

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On the Capacity of DNA-based Data Storage under Substitution Errors

On the Capacity of DNA-based Data Storage under Substitution...

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IEEE visual communications and image processing (VCIP)

作者： Andreas Lenz Paul H. Siegel Antonia Wachter-Zeh Eitan Yaakobi Institute for Communications Engineering Technical University of Munich Germany University of California San Diego California Technion - Israel Institute of Technology Haifa Israel

ISBN: (纸本)9781728173221

Advances in biochemical technologies, such as synthesizing and sequencing devices, have fueled manifold recent experiments on archival digital data storage using DNA. In this paper we review and analyze recent results on information-theoretic aspects of such storage systems. The discussion focuses on a channel model that incorporates the main properties of DNA-based data storage. Namely, the user data is synthesized many times onto a large number of short-length DNA strands. The receiver then draws strands from the stored sequences in an uncontrollable manner. Since the synthesis and sequencing are prone to errors, a received sequence can differ from its original strand, and their relationship is described by a probabilistic channel. Recently, the capacity of this channel was derived for the case of substitution errors inside the sequences. We review the main techniques used to prove a coding theorem and its converse, showing the achievability of the capacity and the fact that it cannot be exceeded. We further provide an intuitive interpretation of the capacity formula for relevant channel parameters, compare with sub-optimal decoding methods, and conclude with a discussion on cost-efficiency.

关键词： Manifolds Sequential analysis image coding visual communication Memory DNA Receivers

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Exploiting Knowledge Embedding to Improve the Description for image Captioning 5th

Exploiting Knowledge Embedding to Improve the Description fo...

引用

5th China conference on Knowledge Graph, and Semantic Computing, CCKS 2020

作者： Song, Dandan Peng, Cuimei Yang, Huan Liao, Lejian Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing Key Laboratory of Intelligent Information Technology School of Computer Science and Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9789811619632

Most existing methods for image captioning are based on the encoder-decoder framework which directly translates visual features into sentences, without exploiting commonsense knowledge available in the form of knowledge graph. Inspired by the success of information retrieval and question answering systems that leverage prior knowledge, we explore a knowledge embedding approach for image captioning. In this paper, we propose a Knowledge Embedding with Attention on Attention (KE-AoA) method for image captioning, which judges whether or how well the objects are related and augments semantic correlations and constraints between them. The KE-AoA method combines knowledge base method (TransE) and text method (Skip-gram), adding external knowledge graph information (triplets) into the language model to guide the learning of word vectors as the regularization term. Then it employs the AoA module to model the relations among different objects. As more inherent relations and commonsense knowledge are learned, the model can generate better image descriptions. The experiments on MSCOCO data sets achieve a significant improvement on the existing methods and validate the effectiveness of our prior knowledge-based approach. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Knowledge graph

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Complex Event Recognition via Spatial-Temporal Relation Graph Reasoning

Complex Event Recognition via Spatial-Temporal Relation Grap...

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IEEE visual communications and image processing (VCIP)

作者： Huan Lin Hongtian Zhao Hua Yang Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781728173221

Events in videos usually contain a variety of factors: objects, environments, actions, and their interaction relations, and these factors as the mid-level semantics can bridge the gap between the event categories and the video clips. In this paper, we present a novel video events recognition method that uses the graph convolution networks to represent and reason the logic relations among the inner factors. Considering that different kinds of events may focus on different factors, we especially use the transformer networks to extract the spatial-temporal features drawing upon the attention mechanism that can adaptively assign weights to concerned key factors. Although transformers generally rely more on large datasets, we show the effectiveness of applying a 2D convolution backbone before the transformers. We train and test our framework on the challenging video event recognition dataset UCF-Crime and conduct ablation studies. The experimental results show that our method achieves state-of-the-art performance, outperforming previous principal advanced models with a significant margin of recognition accuracy.

关键词： Convolution visual communication Computational modeling Semantics Gray-scale Transformers Feature extraction

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