Video Frames Research Articles

Exposing Deep Fake Face Detection using LSTM and CNN

The rapid advancement of deep learning techniques, creating realistic multimedia content has become increasingly accessible, leading to the proliferation of DeepFake technology. DeepFake utilizes generative deep learning algorithms to produce or modify face features in a highly realistic manner, often making it challenging to differentiate between real and manipulated media. This technology, while beneficial in fields such as entertainment and education, also poses significant threats, including misinformation and identity theft. Consequently, detecting DeepFakes has become a critical area of research. In this paper, we propose a novel approach to DeepFake face detection by integrating Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) networks. Our method leverages the strengths of CNNs in spatial feature extraction and LSTMs in temporal sequence modeling to enhance detection accuracy. The CNN component captures intricate facial features, while the LSTM analyzes the temporal dynamics of video frames. We evaluate our model on several benchmark datasets, including Celeb-DF (v2), DeepFake Detection Challenge Preview, and FaceForensics++. Experimental results demonstrate that our hybrid CNN-LSTM model achieves state-of-the-art performance, surpassing existing methods in both accuracy and robustness. This study highlights the potential of combining CNN and LSTM architectures for effective DeepFake detection, contributing to the ongoing efforts to safeguard against digital media manipulation

Automated tear film break-up time measurement for dry eye diagnosis using deep learning

In the realm of ophthalmology, precise measurement of tear film break-up time (TBUT) plays a crucial role in diagnosing dry eye disease (DED). This study aims to introduce an automated approach utilizing artificial intelligence (AI) to mitigate subjectivity and enhance the reliability of TBUT measurement. We employed a dataset of 47 slit lamp videos for development, while a test dataset of 20 slit lamp videos was used for evaluating the proposed approach. The multistep approach for TBUT estimation involves the utilization of a Dual-Task Siamese Network for classifying video frames into tear film breakup or non-breakup categories. Subsequently, a postprocessing step incorporates a Gaussian filter to smooth the instant breakup/non-breakup predictions effectively. Applying a threshold to the smoothed predictions identifies the initiation of tear film breakup. Our proposed method demonstrates on the evaluation dataset a precise breakup/non-breakup classification of video frames, achieving an Area Under the Curve of 0.870. At the video level, we observed a strong Pearson correlation coefficient (r) of 0.81 between TBUT assessments conducted using our approach and the ground truth. These findings underscore the potential of AI-based approaches in quantifying TBUT, presenting a promising avenue for advancing diagnostic methodologies in ophthalmology.

Fall Detection and Boundary Detection in Care Homes

The elderly population represents a significant and rapidly expanding demographic, with a majority experiencing frequent daily accidents, notably falls. Falls rank as the second leading cause of accidental injury deaths globally. To address this issue, we propose a video classification system designed specifically for fall detection. Our fall detection framework comprises two key steps: firstly, the detection of human posture within video frames, followed by fall classification using Convolutional Neural Networks (CNNs). Additionally, we introduce a novel approach for boundary detection, utilizing object detection techniques beyond a predefined line of surveillance captured by a single camera. Through this integrated methodology, we aim to enhance fall detection and boundary breach detection capabilities, thereby contributing to the advancement of elderly care and safety. (Abstract).

Review: Multiple Objects Detection and Tracking using Deep Learning Approach

Multiple Object Tracking (MOT) is a crucial tool with diverse applications, such as object detection, object counting, and security systems. Precise identification and monitoring of numerous objects are essential in several computer vision uses, such as monitoring, self-driving cars, and computer-human communication. Very little has been done to address occlusion problems in order to enable the best moving object tracking with detection The tracking of visual objects is one of the most important components of computer vision. The process of tracking an object (or a group of objects) across time is called object tracking. Visual object tracking is used to identify or link target items over successive video frames. In this study, we analyze the tracking-by-detection strategy, which includes YOLO-based detection and SORT-based tracking. This work elucidates a general approach to tracking and recognizing many objects with an emphasis on accuracy improvement. We aim to revolutionize computer vision by applying Non-Maximum Suppression (NMS) and Intersection over Union (IoU) approaches, and by combining the state-of-the-art YOLO NAS algorithm with conventional tracking methods or an alternative version of the YOLO Algorithm for object identification. It is expected that our work will have a major impact on many different applications, enabling more precise and reliable object tracking and detection in difficult real-world scenarios. Keywords—Multiple Object Detection, Kalman Filters Multiple Object Tracking, DeepSORT, YOLO, IoU, NMS.

GPT-4V Takes the Wheel: Promises and Challenges for Pedestrian Behavior Prediction

Predicting pedestrian behavior is the key to ensure safety and reliability of autonomous vehicles. While deep learning methods have been promising by learning from annotated video frame sequences, they often fail to fully grasp the dynamic interactions between pedestrians and traffic, crucial for accurate predictions. These models also lack nuanced common sense reasoning. Moreover, the manual annotation of datasets for these models is expensive and challenging to adapt to new situations. The advent of Vision Language Models (VLMs) introduces promising alternatives to these issues, thanks to their advanced visual and causal reasoning skills. To our knowledge, this research is the first to conduct both quantitative and qualitative evaluations of VLMs in the context of pedestrian behavior prediction for autonomous driving. We evaluate GPT-4V(ision) on publicly available pedestrian datasets: JAAD and WiDEVIEW. Our quantitative analysis focuses on GPT-4V's ability to predict pedestrian behavior in current and future frames. The model achieves a 57% accuracy in a zero-shot manner, which, while impressive, is still behind the state-of-the-art domain-specific models (70%) in predicting pedestrian crossing actions. Qualitatively, GPT-4V shows an impressive ability to process and interpret complex traffic scenarios, differentiate between various pedestrian behaviors, and detect and analyze groups. However, it faces challenges, such as difficulty in detecting smaller pedestrians and assessing the relative motion between pedestrians and the ego vehicle.

Self-supervised dynamic and static feature representation learning method for flotation monitoring

Froth flotation is a widely used beneficiation method in mineral processing. Accurate grade monitoring is crucial for efficient minerals separation. However, despite recent developments highlighting the potential of deep learning in flotation monitoring, these methods face limitations in application due to the lack of reliable feature extraction and the scarcity of large datasets related to target task. To address these issues, we propose a self-supervised dynamic and static feature collaborative representation learning method. It extracts the spatiotemporal information from froth videos based on a two-stream convolutional architecture and is trained on a video frame prediction task without labeled data until both appearance and motion constraints are satisfied. Experiments on industrial zinc flotation data demonstrate that the learned representations transfer well to downstream grade monitoring tasks. This approach not only solves the label shortage problem when training large-scale deep learning models but also outperforms the state-of-the-art methods in flotation monitoring.

Dual-Modality Cross-Interaction-Based Hybrid Full-Frame Video Stabilization

This study aims to generate visually useful imagery by preventing cropping while maintaining resolution and minimizing the degradation of stability and distortion to enhance the stability of a video for Augmented Reality applications. The focus is placed on conducting research that balances maintaining execution speed with performance improvements. By processing Inertial Measurement Unit (IMU) sensor data using the Versatile Quaternion-based Filter algorithm and optical flow, our research first applies motion compensation to frames of input video. To address cropping, PCA-flow-based video stabilization is then performed. Furthermore, to mitigate distortion occurring during the full-frame video creation process, neural rendering is applied, resulting in the output of stabilized frames. The anticipated effect of using an IMU sensor is the production of full-frame videos that maintain visual quality while increasing the stability of a video. Our technique contributes to correcting video shakes and has the advantage of generating visually useful imagery at low cost. Thus, we propose a novel hybrid full-frame video stabilization algorithm that produces full-frame videos after motion compensation with an IMU sensor. Evaluating our method against three metrics, the Stability score, Distortion value, and Cropping ratio, results indicated that stabilization was more effectively achieved with robustness to flow inaccuracy when effectively using an IMU sensor. In particular, among the evaluation outcomes, within the “Turn” category, our method exhibited an 18% enhancement in the Stability score and a 3% improvement in the Distortion value compared to the average results of previously proposed full-frame video stabilization-based methods, including PCA flow, neural rendering, and DIFRINT.

Parallel Spatio-Temporal Attention Transformer for Video Frame Interpolation

Traditional video frame interpolation methods based on deep convolutional neural networks face challenges in handling large motions. Their performance is limited by the fact that convolutional operations cannot directly integrate the rich temporal and spatial information of inter-frame pixels, and these methods rely heavily on additional inputs such as optical flow to model motion. To address this issue, we develop a novel framework for video frame interpolation that uses Transformer to efficiently model the long-range similarity of inter-frame pixels. Furthermore, to effectively aggregate spatio-temporal features, we design a novel attention mechanism divided into temporal attention and spatial attention. Specifically, spatial attention is used to aggregate intra-frame information, integrating both attention and convolution paradigms through the simple mapping approach. Temporal attention is used to model the similarity of pixels on the timeline. This design achieves parallel processing of these two types of information without extra computational cost, aggregating information in the space–time dimension. In addition, we introduce a context extraction network and multi-scale prediction frame synthesis network to further optimize the performance of the Transformer. Our method and state-of-the-art methods are extensively quantitatively and qualitatively experimented on various benchmark datasets. On the Vimeo90K and UCF101 datasets, our model achieves improvements of 0.09 dB and 0.01 dB in the PSNR metrics over UPR-Net-large, respectively. On the Vimeo90K dataset, our model outperforms FLAVR by 0.07 dB, with only 40.56% of its parameters. The qualitative results show that for complex and large-motion scenes, our method generates sharper and more realistic edges and details.

Consistent movement of viewers' facial keypoints while watching emotionally evocative videos.

Neuropsychological research aims to unravel how diverse individuals' brains exhibit similar functionality when exposed to the same stimuli. The evocation of consistent responses when different subjects watch the same emotionally evocative stimulus has been observed through modalities like fMRI, EEG, physiological signals and facial expressions. We refer to the quantification of these shared consistent signals across subjects at each time instant across the temporal dimension as Consistent Response Measurement (CRM). CRM is widely explored through fMRI, occasionally with EEG, physiological signals and facial expressions using metrics like Inter-Subject Correlation (ISC). However, fMRI tools are expensive and constrained, while EEG and physiological signals are prone to facial artifacts and environmental conditions (such as temperature, humidity, and health condition of subjects). In this research, facial expression videos are used as a cost-effective and flexible alternative for CRM, minimally affected by external conditions. By employing computer vision-based automated facial keypoint tracking, a new metric similar to ISC, called the Average t-statistic, is introduced. Unlike existing facial expression-based methodologies that measure CRM of secondary indicators like inferred emotions, keypoint, and ICA-based features, the Average t-statistic is closely associated with the direct measurement of consistent facial muscle movement using the Facial Action Coding System (FACS). This is evidenced in DISFA dataset where the time-series of Average t-statistic has a high correlation (R2 = 0.78) with a metric called AU consistency, which directly measures facial muscle movement through FACS coding of video frames. The simplicity of recording facial expressions with the automated Average t-statistic expands the applications of CRM such as measuring engagement in online learning, customer interactions, etc., and diagnosing outliers in healthcare conditions like stroke, autism, depression, etc. To promote further research, we have made the code repository publicly available.

Proactive hybrid learning framework for real-time multi-vehicle detection in unregulated traffic environments

Reliable multi-vehicle detection in unregulated traffic environments is a crucial computer vision task in the development of Intelligent Transportation Systems (ITS). Despite the promising potential of Deep Learning (DL) methods for vehicle detection, the presence of uncertainties such as varying vehicle shapes and sizes, intricate background clutter, and unpredictable vehicle flow contribute to the chaotic unregulated traffic. For real-time vehicle detection in an unregulated traffic environment this paper proposes the Hybrid Learning Multi-Vehicle Detection framework (HL-MVD) based on the Convolutional Multi-head Attention Transformer Detector (CMATDet). The primary objective of this study is to generate a dataset containing highly informative video frames using a Pool-based Active Learning Strategy (PALS). Additionally, transfer learning will be implemented to train CMATDet to achieve improved accuracy and reduced detection latency. The proposed approach restructures the baseline YOLOv5x and incorporates a Multi-head Attention transformer encoder to effectively extract global features and a Scale Specific Bidirectional Feature Pyramid Network (SS-BiFPN) to facilitate multi-scale feature representation. Simplified Optimal Transport Algorithm with top-q approximation technique (Sim-OTA) is utilized for label assignment approach. Heatmap analysis demonstrated the suitability of the newly generated dataset “AU-INV-P-PALS” for detecting specific Indian native vehicles. The performance of the proposed framework is evaluated on our custom-developed AU-INV-P-PALS vehicle dataset and the IITM-HeTra Dataset 1. In comparison with contemporary detection models, the proposed HL-MVD framework resulted with higher mAP scores (91.1% on mAP@0.5 and 78.3% on mAP@0.5:0.95) for AU-INV-P-PALS. The proposed model demonstrated lower inference latency (8.1 ms), higher precision score (82.7% for IoU = 0.5), and higher recall score (90.8% for IoU = 0.5) than recent deep learning-based detection models in the literature. The top-q approximation technique in the detection head results in a reduced false-positive rate compared to conventional models. Finally, the performance of the proposed framework is tested on CCTV traffic footage captured on city roads in Chennai, India.

Looking beyond input frames: Self-supervised adaptation for video super-resolution

Recent test-time adaptive video super-resolution (VSR) methods have elevated the performance by exploiting the self-similar patches within the low-resolution (LR) frames to adapt to given input frames. However, the LR frames contain a limited amount of such patches, limiting the performance of such adaptation methods, especially for a challenging scale factor (e.g., ×4). In this work, we propose to explore beyond the input LR frames. In particular, we observe that a greater amount of self-similar patches across various scales can be found from estimated high-resolution (HR) frames (i.e., initially restored frames) produced by a pre-trained VSR network. Upon the observation, we propose a new self-supervision test-time adaptation approach via self-distillation to exploit such rich amount of self-similar patches from initially restored frames. Specifically, we perform self-distillation by exploiting multi-scale relationship: distilling knowledge from larger patches to smaller ones with similar semantics. Our framework is flexible and effective as the knowledge can be distilled either from the network itself or the larger one. Furthermore, our framework demonstrates the robustness, being able to recover from undesirable artifacts present in initially restored frames. Extensive evaluation with various VSR networks on numerous datasets reveals that our algorithm consistently improves the restoration quality by a large margin without ground-truth HR video frames. Code is available at: https://github.com/jinsuyoo/bissa.

Detection of Human in Flame

This report explores the development of a real-time human-in-flames detection system using a YOLOv8 deep learning model. A carefully curated dataset of video frames depicting human-in-flames scenarios with corresponding bounding boxes was utilized to train the model. We explored various hyperparameter configurations and training techniques to optimize its performance. The model's effectiveness was evaluated on a separate validation dataset not used for training. Metrics like accuracy, precision, recall, and F1-score were employed to analyze the model's ability to generalize to unseen scenarios and identify people engulfed in flames within video streams. The findings of this project demonstrate the potential of YOLOv8 for human-in-flames detection, potentially contributing to improved fire safety systems and quicker response times in emergency situations.

Perceiving Actions via Temporal Video Frame Pairs

Video action recognition aims at classifying the action category in given videos. In general, semantic-relevant video frame pairs reflect significant action patterns such as object appearance variation and abstract temporal concepts like speed, rhythm, and so on. However, existing action recognition approaches tend to holistically extract spatiotemporal features. Though effective, there is still a risk of neglecting the crucial action features occurring across frames with a long-term temporal span. Motivated by this, in this article, we propose to perceive actions via frame pairs directly and devise a novel Nest Structure with frame pairs as basic units. Specifically, we decompose a video sequence into all possible frame pairs and hierarchically organize them according to temporal frequency and order, thus transforming the original video sequence into a Nest Structure. Through naturally decomposing actions, the proposed structure can flexibly adapt to diverse action variations such as speed or rhythm changes. Next, we devise a Temporal Pair Analysis module (TPA) to extract discriminative action patterns based on the proposed Nest Structure. The designed TPA module consists of a pair calculation part to calculate the pair features and a pair fusion part to hierarchically fuse the pair features for recognizing actions. The proposed TPA can be flexibly integrated into existing backbones, serving as a side branch to capture various action patterns from multi-level features. Extensive experiments show that the proposed TPA module can achieve consistent improvements over several typical backbones, reaching or updating CNN-based SOTA results on several challenging action recognition benchmarks.

Effective Video Summarization by Extracting Parameter-Free Motion Attention

Video summarization remains a challenging task despite increasing research efforts. Traditional methods focus solely on long-range temporal modeling of video frames, overlooking important local motion information that cannot be captured by frame-level video representations. In this article, we propose the Parameter-free Motion Attention Module (PMAM) to exploit the crucial motion clues potentially contained in adjacent video frames, using a multi-head attention architecture. The PMAM requires no additional training for model parameters, leading to an efficient and effective understanding of video dynamics. Moreover, we introduce the Multi-feature Motion Attention Network (MMAN), integrating the PMAM with local and global multi-head attention based on object-centric and scene-centric video representations. The synergistic combination of local motion information, extracted by the proposed PMAM, with long-range interactions modeled by the local and global multi-head attention mechanism, can significantly enhance the performance of video summarization. Extensive experimental results on the benchmark datasets, SumMe and TVSum, demonstrate that the proposed MMAN outperforms other state-of-the-art methods, resulting in remarkable performance gains.

Laryngeal Cancer Screening During Flexible Video Laryngoscopy Using Large Computer Vision Models.

Develop an artificial intelligence assisted computer vision model to screen for laryngeal cancer during flexible laryngoscopy. Using laryngeal images and flexible laryngoscopy video recordings, we developed computer vision models to classify video frames for usability and cancer screening. A separate model segments any identified lesions on the frames. We used these computer vision models to construct a video stream annotation system. This system classifies findings from flexible laryngoscopy as "potentially malignant" or "probably benign" and segments any detected lesions. Additionally, the model provides a confidence level for each classification. The overall accuracy of the flexible laryngoscopy cancer screening model was 92%. For cancer screening, it achieved a sensitivity of 97.7% and a specificity of 76.9%. The segmentation model attained an average precision at a 0.50 intersection-over-union of 0.595. The confidence level for positive screening results can assist clinicians in counseling patients regarding the findings. Our model is highly sensitive and adequately specific for laryngeal cancer screening. Segmentation helps endoscopists identify and describe potential lesions. Further optimization is required to enable the model's deployment in clinical settings for real-time annotation during flexible laryngoscopy.

Adaptive Processing of Camera Video Stream with Limitations on the Network Data Transmission Bandwidth

Video surveillance systems, cameras, and video stream processing are actively used in many enterprises as a monitoring and control tool for regular and emergency situations, as well as staff activities. The application of intelligent algorithms allows tracking and minimizing operator errors, but these approaches are highly sensitive to the quality of the original video, presence of noise, and low resolution. On the other hand, such video surveillance systems may be limited by network bandwidth. Therefore, this work considers an adaptive video stream processing algorithm that ensures efficient operation of computer vision and object recognition methods while minimizing the amount of transmitted information within network bandwidth constraints. The proposed algorithm addresses the task of determining boundary conditions that ensure the functionality of object recognition algorithms with the least amount of video stream. Corresponding experimental studies were conducted to determine the minimum values of frame resolution and video bitrate. The algorithm was tested in organizing video surveillance at warehouse complexes. The obtained results can be used in developing decision support systems for enterprises in various industries requiring intelligent processing of large volumes of data.

Mean flow generated by asymmetric periodic excitation in an annular channel

Surface waves of a shallow liquid layer confined in an annular channel of length L~\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\ ilde{L}}}$$\\end{document} are generated by periodic acceleration of the channel along its azimuthal direction. We show that if the oscillations are anharmonic, e.g., having the form of a saw tooth, a mean mass flow is generated and even the mean speed over one period is zero. The flow is studied both experimentally and numerically. In the experiment, the velocity field and surface deflection are derived from the frames of videos recorded with a Go-Pro Hero 4 camera with custom rectilinear lens. We show resonance curves of the mean square of the water surface deflection and of the averaged mass flow. Both curves show almost coinciding maxima for multiples of the fundamental resonance frequency f~r=c~0/(2L~),\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\ ilde{f}}}_r=\ ilde{c}_0/(2{{\ ilde{L}}}),$$\\end{document} where c~0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\ ilde{c}}}_0$$\\end{document} is the shallow water wave velocity. The measurements are confirmed by numerical solutions of an integrated boundary layer model including inertia and dissipation.

Surface Bubbles Emergence as an Indicator for Optimal Concrete Compaction.

Compaction quality significantly influences the strength and durability of concrete in structures. Under-compacting can retain entrapped air, reducing strength, while over-compacting can lead to segregation, creating local variances in strength distribution and modulus of elasticity in the concrete structure. This study examines the widely adopted concept that compaction is optimal when bubbles cease to emerge on the concrete surface. We recorded the surface activity of six comparable concrete specimens during the compaction process using a 4K video camera. Four specimens were compacted using a table vibrator and two with a poker vibrator. From the video frames, we isolated the bubbles for analysis, employing digital image processing techniques to distinguish newly risen bubbles per frame. It was found that the bubbles continuously rose to the surface in all specimens throughout the compaction process, suggesting a need for extended compaction, with some specimens showing a slow in the rate of the bubbles' emergence. However, upon examining the segregation levels, it was discovered that all the specimens were segregated, some severely, despite the continued bubble emergence. These findings undermine the reliability of using bubble emergence as a principle to stop compaction and support the need for developing online measurement tools for evaluating compaction quality.

Spatiotemporal Inconsistency Learning and Interactive Fusion for Deepfake Video Detection

With the rise of the metaverse, the rapid advancement of Deepfakes technology has become closely intertwined. Within the metaverse, individuals exist in digital form and engage in interactions, transactions, and communications through virtual avatars. However, the development of Deepfakes technology has led to the proliferation of forged information disseminated under the guise of users’ virtual identities, posing significant security risks to the metaverse. Hence, there is an urgent need to research and develop more robust methods for detecting deep forgeries to address these challenges. This paper explores deepfake video detection by leveraging the spatiotemporal inconsistencies generated by deepfake generation techniques, and thereby proposing the interactive spatioTemporal inconsistency learning and interactive fusion (ST-ILIF) detection method, which consists of phase-aware and sequence streams. The spatial inconsistencies exhibited in frames of deepfake videos are primarily attributed to variations in the structural information contained within the phase component of the Fourier domain. To mitigate the issue of overfitting the content information, a phase-aware stream is introduced to learn the spatial inconsistencies from the phase-based reconstructed frames. Additionally, considering that deepfake videos are generated frame-by-frame and lack temporal consistency between frames, a sequence stream is proposed to extract temporal inconsistency features from the spatiotemporal difference information between consecutive frames. Finally, through feature interaction and fusion of the two streams, the representation ability of intermediate and classification features is further enhanced. The proposed method, which was evaluated on four mainstream datasets, outperformed most existing methods, and extensive experimental results demonstrated its effectiveness in identifying deepfake videos. Our source code is available at https://github.com/qff98/Deepfake-Video-Detection

МЕТОД ДВОКАСКАДНОЇ ІМПЛАНТАЦІЇ ПРИХОВАНОЇ ІНФОРМАЦІЇ НА ОСНОВІ СТЕГАНОКОМПРЕСІЙНИХ ПЕРЕТВОРЕНЬ

In the article, the need for further development of the state is directly related to: solving issues of increasing defense capability and information security; development of information and intelligent systems. The necessity of ensuring the required level of completeness of information, compliance with the requirements of its relevance, achievement and maintenance of the appropriate level of integrity, accessibility and confidentiality is substantiated. The article emphasizes that for this purpose, complex systems of coding and information protection are being built. It is shown that recently, in order to further increase the level of security of information resources, methods of hidden embedding of information have been used. In this field of scientific and applied research, the direction of timely delivery of integral video information in a secure mode is outlined. For protection, the following can be used: meta-messages, which are formed on the basis of intelligent analysis of video frames; separate video segments of aerial photographs containing the most important information for decision-making. This article examines a class of methods of steganographic transformations, which are associated with embedding messages in digital containers, which are formed by a stream of video segments (VS). However, existing steganographic systems are based mainly on the use of the amount of psychovisual (PSV) redundancy available for reduction. Therefore, an increase in steganographic capacity leads to a loss of integrity and efficiency of delivery of video-container information. Steganocompression coding in the stegano-polyadic basis was created on the basis of taking into account the amount of redundancy permissible for reduction. In this case, in the process of compression of the components of the VS, the direct embedding of hidden information is organized. Concealment of information is carried out by technological stages, depending on the compression transformation of the transitional syntax of the quilted sequence. The key stage is the technology of two-stage implementation of the elements of the concealed message added to the stegano sequences to the syntax of the steganocompression representation of the BC components.