Video Frames Research Articles

Take good care of your fish: fish re-identification with synchronized multi-view camera system

IntroductionFish re-identification (re-ID) is of great significance for fish monitoring and can contribute to aquaculture and fish breeding. Synchronizing information from different cameras is beneficial for optimizing re-ID performance.MethodsWe constructed the first underwater fish re-identification benchmark dataset (FS48) under three camera conditions. FS48 encompasses 48 different fish identities, 10,300 frames, and 39,088 bounding boxes, covering various lighting conditions and background environments. Additionally, we developed the first robust and accurate fish re-identification baseline, FSNet, which fuses information from three camera positions by extracting features from synchronized video frames of each position and combining the synchronized information.ResultsThe experimental results show that FS48 is universal and of high quality. FSNet has an effective network design and demonstrates good performance, achieving better re-identification performance by combining information from three positions, helping improve overall re-test accuracy, and evaluating the effectiveness of re-identification among detectors.DiscussionOur dataset will be released upon acceptance of this paper, which is expected to further promote the development of underwater fish re-identification.

Computer-Vision-Aided Deflection Influences Line Identification of Concrete Bridge Enhanced by Edge Detection and Time-Domain Forward Inference

To enhance the accuracy and efficiency of the deflection response measurement of concrete bridges with a non-contact scheme and address the ill-conditioned nature of the inverse problem in influence line (IL) identification, this study introduces a computer-vision-aided deflection IL identification method that integrates edge detection and time-domain forward inference (TDFI). The methodology proposed in this research leverages computer vision technology with edge detection to surpass traditional contact-based measurement methods, greatly enhancing the operational efficiency and applicability of IL identification and, in particular, addressing the challenge of accurately measuring small deflections in concrete bridges. To mitigate the limitations of the Lucas–Kanade (LK) optical flow method, such as unclear feature points within the camera’s field of view and occasional point loss in certain video frames, an edge detection technique is employed to identify maximum values in the first-order derivatives of the image, creating virtual tracking points at the bridge edges through image processing. By precisely defining the bridge boundaries, only the essential structural attributes are preserved to enhance the reliability of minimal deflection deformations under vehicular loads. To tackle the ill-posed nature of the inverse problem, a TDFI model is introduced to identify IL, recursively capturing the static bridge response generated by the bridge under the influence of successive axles of a multi-axle vehicle. The IL is then computed by dividing the response by the weight of the preceding axle. Furthermore, an axle weight ratio reduction coefficient is proposed to mitigate noise amplification issues, ensuring that the weight of the preceding axle surpasses that of any other axle. To validate the accuracy and robustness of the proposed method, it is applied to numerical examples of a simply supported concrete beam, indoor experiments on a similar beam, and field tests on a three-span continuous concrete beam bridge.

ACORN+: Adaptive Compression-Reconstruction for Device-Cloud Collaboration Video Services

With the improvement of edge-based autonomous systems such as mobile Industrial IoT(IIoT) networks, edge devices can capture and upload videos with increasing bitrates. Massive edge-computing end nodes are eager for adequate multimedia data to satisfy the requirements of real-time video services. However, existing encoding standards for video services in Web 2.0 are specifically designed for something other than IoT video streaming. We have improved our Adaptive Compression-Reconstruction framework ACORN to obtain ACORN+, based on compressed sensing and recent advances in deep learning. At end nodes, we compress multiple sequential video frames into a single frame to reduce video volume. Given that multiple kinds of intelligent tasks are expected to be finished on the device side, we also designed a device-cloud collaboration scheme where deep learning-based algorithms can be executed on both the device and server sides. Experiments reveal that video analytics can be conducted on compressed frames. Taking action recognition as a device-cloud collaboration use case, we find ACORN+ obtains more than 3x speedup on compressed frames. The reconstruction algorithm in ACORN+ is with 1- 4 dB improvements. Moreover, the encoding time cost and the encoded video volume are reduced by more than 4x under the ACORN+ framework. 1

Vapor Channel Oscillations in Laser Lithotripsy.

Laser-based endoscopic procedures present special challenges to deliver energy for ablation or coagulation of target tissues. When optical fiber-target quasi-contact (< 0.5 mm distance) cannot be maintained or is undesirable, the creation of intervening vapor bubbles and channels provide for the necessary transmission of laser energy to the target. This work investigates the characteristics and the dynamics of vapor channels that directly affect ablation efficiency and ablation rate and are known to effect stone movement, all of which impact procedure efficiency and safety. A simplified, experimental model for thulium fiber laser (1940 nm) lithotripsy consists of a water-filled cuvette and a vertically oriented laser fiber (200 μm core diameter) with its tip at 9 mm for "quasi-free" bubble generation and at vapor channel working distances 1-5 mm from and centered on the transparent cuvette bottom simulating a target's surface. Laser power transmission is recorded and synchronized with video frames from a high-speed camera (24,260 frames per second) to capture the induced vapor channels' and bubbles' development. Laser-induced channel transmission from 0% to 100% for 1, 2, and 3 mm fiber-target distances undergoes oscillations with average periods of 0.32, 0.64, and 1.0 ms, respectively, for 500 W laser output power. For fixed fiber-target distances of 0.5, 1, and 2 mm, the variation of these average oscillation frequencies across laser powers from 500 to 1000 W is much smaller, not exceeding 14%. For fiber-target distances in the range of 1-5 mm, the fraction of the 500 W laser's total pulse energy delivered to the target for 1, 2, and 3 ms pulses linearly decreases from 0.78 to less than 0.2. The channel and bubble dynamics begin with a spherical seed bubble expansion centered on the distal fiber tip that evolves into a pear shape whose surface exhibits periodic irregularities attributable to laser beam interruption by water droplets within the developing bubble. The study of laser-induced channel oscillations provides quantitative information relating fiber-target distance to channel oscillation frequency and energy transmission onto a target. These oscillations directly effect ablation efficiency and ablation rates that are important parameters for the optimization of a procedure's safety and duration. Insights that may lead to further reduction in retropulsion are also presented. Lasers Surg. Med. 00:00-00, 2024. © 2024 Wiley Periodicals LLC.

INTELLIGENT VIDEO ANALYSIS TECHNOLOGY FOR AUTOMATIC FIRE CONTROL TARGET RECOGNITION BASED ON MACHINE LEARNING

Context. Target recognition is a priority in military affairs. This task is complicated by the fact that it is necessary to recognize moving objects, different terrain and landscape create obstacles for recognition. Combat actions can take place at different times of the day, accordingly, it is necessary to take into account the perspective of lighting and general lighting. It is necessary to detect the object in the video by segmenting the video frames, recognize and classify. Objective of the study is to develop a technology for the analysis of the development of a technology for recognizing targets in real time as a component of the fire control system, due to the use of artificial intelligence, YOLO and machine learning. Method. The article develops a video stream analysis technology for automatic target recognition of the fire control system based on machine learning. The paper proposes the development of a target recognition module as a component of the fire control system within the framework of the proposed information technology using artificial intelligence. The YOLOv8 pattern recognition model family was used to develop the target recognition module. The methods used during the study of the formed dataset. – Bounding Box: Noise – Up to 15% of pixels (limiting frame: adding salt and pepper noise to the image – up to 15% of pixels). – Bounding Box: Blur – Up to 2.5px (bounding box: adding Gaussian blur to the image – up to 2.5 pixels). – Cutout – 3 boxes with 10% size each (cut out a part of the image – 3 boxes of 10% size each). – Brightness Between –25% and +25% (changing the brightness of the image to increase the resistance of the model to changes in lighting and camera settings – from –25% to +25%). – Rotation – Between –15 and +15 (rotation of the image object – clockwise or counterclockwise by degrees from –15 to +15). – Flip – Horizontal (flip the image object horizontally). Results. The data is collected from open sources, in particular, from videos posted in open sources on the YouTube platform. The main task of data preprocessing is the classification of three classes of objects on video or in real time – APC, BMP and TANK. The dataset is formed using the Roboflow platform based on the labeling tools and subsequently the augmentation tools. The dataset consists of 1193 unique images – approximately equally for each class. The training was conducted using Google Colab resources. It took 100 epochs to train the model. Conclusions. Analysis is performed according to mAP50 (average precision as 0.85), mAP50-95 (0.6), precision (0.89) and recall (0.75). Big losses are due to the fact that the background was not taken into account during the research – training the module on the basis of confirmed data (images) of the background without technology

Text-video retrieval re-ranking via multi-grained cross attention and frozen image encoders

State-of-the-art methods for text-video retrieval generally leverage CLIP embeddings and cosine similarity for efficient retrieval. Meanwhile, recent advancements in cross-attention techniques introduce transformer decoders to facilitate attention computation between text queries and visual tokens extracted from video frames, enabling a more comprehensive interaction between textual and visual information. In this study, we combine the advantages of both approaches and propose a fine-grained re-ranking approach incorporating a multi-grained text-video cross attention module. Specifically, the re-ranker enhances the top K similar candidates identified by the cosine similarity network. To explore video and text interactions efficiently, we introduce frame and video token selectors to obtain salient visual tokens at both frame and video levels. Then, a multi-grained cross-attention mechanism is applied between text and visual tokens at these levels to capture multimodal information. To reduce the training overhead associated with the multi-grained cross-attention module, we freeze the vision backbone and only train the multi-grained cross attention module. This frozen strategy allows for scalability to larger pre-trained vision models such as ViT-G, leading to enhanced retrieval performance. Experimental evaluations on text-video retrieval datasets showcase the effectiveness and scalability of our proposed re-ranker combined with existing state-of-the-art methodologies.

DFCNet +: Cross-modal dynamic feature contrast net for continuous sign language recognition

In sign language communication, the combination of hand signs and facial expressions is used to convey messages in a fluid manner. Accurate interpretation relies heavily on understanding the context of these signs. Current methods, however, often focus on static images, missing the continuous flow and the story that unfolds through successive movements in sign language. To address this constraint, our research introduces the Dynamic Feature Contrast Net Plus (DFCNet+), a novel model that incorporates both dynamic feature extraction and cross-modal learning. The dynamic feature extraction module of DFCNet+ uses dynamic trajectory capture to monitor and record motion across frames and apply key features as an enhancement tool that highlights pixels that are critical for recognizing important sign language movements, allowing the model to follow the temporal variation of the signs. In the cross-modal learning module, we depart from the conventional approach of aligning video frames with textual descriptions. Instead, we adopt a gloss-level alignment, which provides a more detailed match between the visual signals and their corresponding text glosses, capturing the intricate relationship between what is seen and the associated text. The enhanced proficiency of DFCNet+ in discerning inter-frame details translates to heightened precision on benchmarks such as PHOENIX14, PHOENIX14-T and CSL-Daily. Such performance underscores its advantage in dynamic feature capture and inter-modal learning compared to conventional approaches to sign language interpretation. Our code is available at https://github.com/fyzjut/DFCNet_Plus.

Key Frame Selection for Temporal Graph Optimization of Skeleton-Based Action Recognition

Graph neural networks (GNNs) are extensively utilized to capture the spatial–temporal relationships among human body parts for skeleton-based action recognition. However, due to the inefficient information propagation caused by redundant sampling of video frames in the temporal domain, we focus on refining temporal graphs through key frame selection. To this end, we propose a multi-stage key frame selection (MSKFS) method, aiming to find the most representative frames as the graph nodes to learn compact temporal graph representations of human skeletons for action recognition. The MSKFS progressively selects key frames in two stages: (1) salient posture frame selection based on the global dynamics of body parts and (2) key frame refinement and alignment according to intra-frame correlations. The first stage captures the most salient information and aligns the corresponding information of skeleton sequences within the same category. The second stage enriches the subtle information for the integrity of the information derived by the salient frames. Moreover, variational inference is applied to differentiate the key frame refinement and alignment procedure, allowing the end-to-end optimization of arbitrary graph-based models to represent the obtained compact graph for skeleton-based action recognition. Our MSKFS method achieves state-of-the-art performances on two challenging action recognition datasets.

무인 점포 실시간 이상 행동 감지를 위한 Transformer 기반지능형 CCTV 시스템

The rapid expansion of unmanned retail stores has raised critical security concerns, thereby necessitating the development and implementation of robust protective measures. The absence of real-time monitoring systems in these environments has heightened the vulnerability to risks such as theft and property damage. Although closed-circuit television (CCTV) systems have been deployed to retrospectively investigate criminal activities, these systems are often insufficient in preventing incidents. This study introduces a Transformer-based intelligent CCTV system designed for the real-time detection of anomalous behaviors within unmanned retail environments. Unlike conventional systems that rely on basic machine learning models, our proposed system leverages human joint position data extracted from CCTV footage to classify a range of anomalous behaviors, including theft, falls, and property damage. Additionally, extensive hyperparameter optimization was performed to maximize the model's effectiveness in these specific environments. Our System enhances the system's usability by enabling real-time identification of anomalous behavior, complete with location data, timestamps, and corresponding video frame sequences.

Deep learning enabled exercise monitoring system for sustainable online education of future teacher-trainers

IntroductionIn recent years, the importance of effective training methods for future physical education teacher-trainers has grown significantly, particularly in the context of online education.MethodsThis research introduces a pioneering Deep Learning Enabled Exercise Monitoring System aimed at enhancing the online education experience for these trainers. The study employs a skeleton-based approach utilizing the PoseNet model to monitor and provide real-time feedback on physical exercises such as pull-ups, push-ups, sit-ups, squats, and bicep workouts. The system achieves a remarkable accuracy rate of 99.8% by analyzing key skeletal points extracted from video frames, addressing the challenge of ensuring correct exercise execution without physical supervision–a common issue in remote learning environments.ResultsTo validate the system’s effectiveness, data was collected through a series of controlled experiments involving various exercises. The system’s design focuses on low-resource requirements, making it accessible and sustainable for diverse educational contexts.DiscussionThe findings demonstrate the system’s potential to revolutionize online physical education by offering a balance of technological innovation and educational utility. This research not only elevates the quality of training for future educators but also contributes to the broader field of sustainable digital education technologies.

Efficient Vehicle Detection and Optimization in Multi-Graph Mode Considering Multi-Section Tracking Based on Geographic Similarity

Vehicle detection is an important part of modern intelligent transportation systems. At present, complex deep learning algorithms are often used for vehicle detection and tracking, but high-precision detection results are often obtained at the cost of time, and the existing research rarely considers optimization algorithms for vehicle information. Based on this, we propose an efficient method for vehicle detection in multi-graph mode and optimization method considering multi-section tracking based on geographic similarity. In this framework, we design a vehicle extraction method based on multi-graph mode and a vehicle detection technology based on traffic flow characteristics, which can cope with the challenge of vehicle detection under an unstable environment. Further, a multi-section tracking optimization technology based on geographic similarity at a high video frame rate is proposed, which can efficiently identify lane change behavior and match, track, and optimize vehicles. Experiments are carried out on several road sections, and the model performance and optimization effect are analyzed. The experimental results show that the vehicle detection and optimization algorithm proposed in this paper has the best effect and high detection accuracy and robustness. The average results of Recall, Precision, and F1 are 0.9715, 0.979, and 0.9752, respectively, all of which are above 0.97, showing certain competitiveness in the field of vehicle detection.

Field geology under the sea with a remotely operated vehicle: Mona Rift, Puerto Rico

We implemented concepts of field geology at great ocean depths by constructing virtual outcrops from a string of overlapping video frames collected by remotely operated vehicles (ROVs). This lower-cost alternative to drilling boreholes allows stratigraphic extension into the offshore and regional interpretation of marine seismic profiles. The imagery was collected along a dive transect on the western wall of Mona Rift, a deep and narrow rift northwest of Puerto Rico, between water depths of 1560 m and 3927 m. The northern coast of Puerto Rico and its large offshore area are underlain by a mid-Eocene and younger forearc basin topped by a thick carbonate platform. There are no drill holes offshore, and tying seismic lines across the shoreline there is problematic. We describe our virtual outcrop and constrain its age and stratigraphy using seven rock samples collected by ROV and compare the outcrop’s stratigraphy to deep boreholes and outcrops on land. Our formation descriptions and ages agree, for the most part, with those on land, but we identified a 100-m-thick section that is represented on land by an unconformity. Our stratigraphic interpretation indicates lateral variations in formation thicknesses and establishes a cross-section for additional sampling of the Eocene–Pliocene geology. It also suggests that Mona Rift has formed since the mid-Pliocene. The presence or absence of ferromanganese (Fe-Mn) crust on rocks along the transect may be correlated with the smoothness of the rock surface.

Noise & mottle suppression methods for cumulative Cherenkov images of radiation therapy delivery.

Cherenkov imaging during radiotherapy provides a real time visualization of beam delivery on patient tissue, which can be used dynamically for incident detection or to review a summary of the delivered surface signal for treatment verification. Very few photons form the images, and one limitation is that the noise level per frame can be quite high, and mottle in the cumulative processed images can cause mild overall noise. This work focused on removing or suppressing noise via image postprocessing. Images were analyzed for peak-signal-to-noise and spatial frequencies present, and several established noise/mottle reduction algorithms were chosen based upon these observations. These included total variation minimization (TV-L1), non-local means filter (NLM), block-matching 3D (BM3D), alpha (adaptive) trimmed mean (ATM), and bilateral filtering. Each were applied to images acquired using a BeamSite camera (DoseOptics) imaged signal from 6x photons from a TrueBeam linac delivering dose at 600 MU/min incident on an anthropomorphic phantom and tissue slab phantom in various configurations and beam angles. The standard denoised images were tested for PSNR, noise power spectrum (NPS) and image sharpness. The average peak-signal-to-noise ratio (PSNR) increase was 17.4% for TV-L1. NLM denoising increased the average PSNR by 19.1%, BM3D processing increased it by12.1% and the bilateral filter increased the average PSNR by 19.0%. Lastly, the ATM filter resulted in the lowest average PSNR increase of 10.9%. Of all of these, the NLM and bilateral filters produced improved edge sharpness with, generally, the lowest NPS curve. For cumulative image Cherenkov data, NLM and the bilateral filter yielded optimal denoising with the TV-L1 algorithm giving comparable results. Single video frame Cherenkov images exhibit much higher noise levels compared to cumulative images. Noise suppression algorithms for these frame rates will likely be a different processing pipeline involving these filters incorporated with machine learning.

Cloud IaaS Optimization Using Machine Vision at the IoT Edge and the Grid Sensing Algorithm

Security grids consisting of High-Definition (HD) Internet of Things (IoT) cameras are gaining popularity for organizational perimeter surveillance and security monitoring. Transmitting HD video data to cloud infrastructure requires high bandwidth and more storage space than text, audio, and image data. It becomes more challenging for large-scale organizations with massive security grids to minimize cloud network bandwidth and storage costs. This paper presents an application of Machine Vision at the IoT Edge (Mez) technology in association with a novel Grid Sensing (GRS) algorithm to optimize cloud Infrastructure as a Service (IaaS) resource allocation, leading to cost minimization. Experimental results demonstrated a 31.29% reduction in bandwidth and a 22.43% reduction in storage requirements. The Mez technology offers a network latency feedback module with knobs for transforming video frames to adjust to the latency sensitivity. The association of the GRS algorithm introduces its compatibility in the IoT camera-driven security grid by automatically ranking the existing bandwidth requirements by different IoT nodes. As a result, the proposed system minimizes the entire grid’s throughput, contributing to significant cloud resource optimization.

An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection

Video deepfake detection has emerged as a critical field within the broader domain of digital technologies driven by the rapid proliferation of AI-generated media and the increasing threat of its misuse for deception and misinformation. The integration of Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM) has proven to be a promising approach for improving video deepfake detection, achieving near-perfect accuracy. CNNs enable the effective extraction of spatial features from video frames, such as facial textures and lighting, while LSTM analyses temporal patterns, detecting inconsistencies over time. This hybrid model enhances the ability to detect deepfakes by combining spatial and temporal analysis. However, the existing research lacks systematic evaluations that comprehensively assess their effectiveness and optimal configurations. Therefore, this paper provides a comprehensive review of video deepfake detection techniques utilising hybrid CNN-LSTM models. It systematically investigates state-of-the-art techniques, highlighting common feature extraction approaches and widely used datasets for training and testing. This paper also evaluates model performance across different datasets, identifies key factors influencing detection accuracy, and explores how CNN-LSTM models can be optimised. It also compares CNN-LSTM models with non-LSTM approaches, addresses implementation challenges, and proposes solutions for them. Lastly, open issues and future research directions of video deepfake detection using CNN-LSTM will be discussed. This paper provides valuable insights for researchers and cyber security professionals by reviewing CNN-LSTM models for video deepfake detection contributing to the advancement of robust and effective deepfake detection systems.

Deep‐Learning‐Based Facial Retargeting Using Local Patches

AbstractIn the era of digital animation, the quest to produce lifelike facial animations for virtual characters has led to the development of various retargeting methods. While the retargeting facial motion between models of similar shapes has been very successful, challenges arise when the retargeting is performed on stylized or exaggerated 3D characters that deviate significantly from human facial structures. In this scenario, it is important to consider the target character's facial structure and possible range of motion to preserve the semantics assumed by the original facial motions after the retargeting. To achieve this, we propose a local patch‐based retargeting method that transfers facial animations captured in a source performance video to a target stylized 3D character. Our method consists of three modules. The Automatic Patch Extraction Module extracts local patches from the source video frame. These patches are processed through the Reenactment Module to generate correspondingly re‐enacted target local patches. The Weight Estimation Module calculates the animation parameters for the target character at every frame for the creation of a complete facial animation sequence. Extensive experiments demonstrate that our method can successfully transfer the semantic meaning of source facial expressions to stylized characters with considerable variations in facial feature proportion.

LiqD: Dynamic Liquid Level Detection under Containers

Abstract. In daily life and industrial production, it is crucial to accurately detect changes in liquid level in containers. Traditional contact measurement methods have some limitations, while emerging non-contact image processing technology shows good application prospects. This paper proposes a container dynamic liquid level detection model based on U-Net. This model uses the SAM model to generate an initial data set, and then evaluates and filters out high-quality pseudo-label images through the semi-supervised framework to build an exclusive data set. The model uses U-Net to extract masking images of containers from the data set, and uses morphological processing to compensate for masking defects. Subsequently, the model calculates the grayscale difference between adjacent video frame images at the same position, segments the liquid level change area by setting a difference threshold, and finally uses a lightweight neural network to classify the liquid level state and achieves the accuracy of 91.39%. This approach not only mitigates the impact of intricate surroundings, but also reduces the demand for training data, showing strong robustness and versatility. A large number of experimental results show that the proposed model can effectively detect the dynamic liquid level changes of the liquid in the container, providing a novel and efficient solution for related fields.

Fusion Attention for Action Recognition: Integrating Sparse-Dense and Global Attention for Video Action Recognition

Conventional approaches to video action recognition perform global attention over the entire video patches, which may be ineffective due to the temporal redundancy of video frames. Recent works on masked video modeling adopt a high-ratio tube masking and reconstruction strategy as a pre-training method to mitigate the problem of focusing on spatial features well but not on temporal features. Inspired by this pre-training method, we propose Fusion Attention for Action Recognition (FAR), which fuses the sparse-dense attention patterns specialized for temporal features with global attention during fine-tuning. FAR has three main components: head-split sparse-dense attention (HSDA), token–group interaction, and group-averaged classifier. First, HSDA splits the head of multi-head self-attention to fuse global and sparse-dense attention. The sparse-dense attention is divided into groups of tube-shaped patches to focus on temporal features. Second, token–group interaction is used to improve information exchange between divided patch groups. Finally, the group-averaged classifier uses spatio-temporal features from different patch groups to improve performance. The proposed method uses the weight parameters that are pre-trained with VideoMAE and MVD, and achieves higher performance (+0.1–0.4%) with less computation than models fine-tuned with global attention on Something-Something V2 and Kinetics-400. Moreover, qualitative comparisons show that FAR captures temporal features quite well in highly redundant video frames. The FAR approach demonstrates improved action recognition with efficient computation, and exploring its adaptability across different pre-training methods presents an interesting direction for future research.

A Decentralized Digital Watermarking Framework for Secure and Auditable Video Data in Smart Vehicular Networks

Thanks to the rapid advancements in Connected and Automated Vehicles (CAVs) and vehicular communication technologies, the concept of the Internet of Vehicles (IoVs) combined with Artificial Intelligence (AI) and big data promotes the vision of an Intelligent Transportation System (ITS). An ITS is critical in enhancing road safety, traffic efficiency, and the overall driving experience by enabling a comprehensive data exchange platform. However, the open and dynamic nature of IoV networks brings significant performance and security challenges to IoV data acquisition, storage, and usage. To comprehensively tackle these challenges, this paper proposes a Decentralized Digital Watermarking framework for smart Vehicular networks (D2WaVe). D2WaVe consists of two core components: FIAE-GAN, a novel feature-integrated and attention-enhanced robust image watermarking model based on a Generative Adversarial Network (GAN), and BloVA, a Blockchain-based Video frames Authentication scheme. By leveraging an encoder–noise–decoder framework, trained FIAE-GAN watermarking models can achieve the invisibility and robustness of watermarks that can be embedded in video frames to verify the authenticity of video data. BloVA ensures the integrity and auditability of IoV data in the storing and sharing stages. Experimental results based on a proof-of-concept prototype implementation validate the feasibility and effectiveness of our D2WaVe scheme for securing and auditing video data exchange in smart vehicular networks.

A vibration transducer framework for the measurement and reconstruction of vibration signals via low-rate sampling

This study presents a novel, cost-effective, and highly accurate computer vision-based vibration transducer framework, enabling the measurement and reconstruction of vibration signals using video frames captured at a low frame per second (fps) relative to the Nyquist rate of the measured signal. In the proposed framework, an assembly consisting of several spring-mass systems serves as the primary sensor, while a camera module tracks the motion of the seismic masses as the secondary sensor. The setup, along with the modified compressive sensing equation (CS) equation, results in a transducer scheme that can recover the base motion signals from the displacement history of seismic masses sampled at rates much lower than the maximum predominant frequency of the original signal. The performance of the proposed transducer framework has been successfully verified by numerical simulations. In addition, an experimental study has been conducted in order to validate the concept under modelling and measurement error.