Fuzzy-Based Digital Video Stabilization in Static Scenes

Capturing or acquiring video data from advanced gadgets, like cell phone cameras, smart phones, camcorders, etc., is very recent and up growing trend. With the advancement of the video camera as the integrated part of these multimedia devices generates necessity for video stabilization. Digital video stabilization or video frame alignment techniques are used to improve quality of input video by removing the undesired camera motions jitters; undesirable jiggles etc. Many video stabilization techniques are developed till now but only fast and low computational complexity approaches can be applied on the embedded devices. The paper presents the review of fast block matching local motion vector estimation and alignment algorithm. Effectiveness of the algorithm is shown by quantitative evaluation. The proposed work will also help to boost the performance of various error concealment, error resilience and video quality improvement techniques in H.264/HEVC video codec.

Video stabilization is an instrumental and a very commonly employed image processing operations, yet still a very active area of research. Although lots of work have been done in the field of Video Stabilization, however still not much work has been done in the field of Real time Video Stabilization, especially works dealing with implementation of Technology on prototyping Boards. This work discusses the implementation of a Real time implementation of a Digital Video Stabilization system based on Upright Speeded up Robust Features mechanism. The U-SURF used in the algorithm has been found to be computationally much lighter as compared to the existing proposed mechanisms and has been implemented using Verilog as the preferred HDL Language. The developed architecture synthesized for Xilinx Zynq XC7030 has the significant benefits of being a fully pipelined architecture, making it much faster than the existing systems and at the same time exhibits better PSNR and MSE values. The system has been meticulously tested on 3 different standard Datasets namely StableNet Dataset, StabNet Dataset and the MATLAB Computer Vision Toolbox Dataset.

Assessing the quality of the motion stability is important to evaluating the performance of video stabilization algorithms. This paper presents a novel quality assessment scheme for the video motion stability in a full-reference (FR) manner. Given ideally stable videos and their corresponding shaky videos, our method measures the geodesic distance between motion paths of the stable and the stabilized videos. Due to the use of the Riemannian metric defined on the manifold of spatial transformations, our method enables the intrinsic and faithful measurement on pairwise motion disparities. To facilitate the FR assessment, a data set of stable and shaky videos is constructed by directly capturing realistic stable/shaky videos with a customized device. Then, digital video stabilization algorithms can be run on shaky videos to obtain the stabilized sequence of frames, whereupon their performances are evaluated by using our stability assessment. The experiments demonstrate that our stability assessment gains good concordance with the subjective assessment.

Video stabilization aims to eliminate camera jitter and improve the visual experience of shaky videos. Video stabilization methods often ignore the active movement of the foreground objects and the camera, and may result in distortion and over-smoothing problems. To resolve these issues, this paper proposes a novel video stabilization method based on motion decomposition. Since the inter-frame movement of foreground objects is different from that of the background, we separate foreground feature points from background feature points by modifying the classic density based spatial clustering method of applications with noise (DBSCAN). The movement of background feature points is consistent with the movement of the camera, which can be decomposed into the camera jitter and the active movement of the camera. And the movement of foreground feature points can be decomposed into the movement of the camera and the active movement of foreground objects. Based on motion decomposition, we design first-order and second-order trajectory smoothing constraints to eliminate the high-frequency and low-frequency components of the camera jitter. To reduce content distortion, shape-preserving constraints, and regularization constraints are taken to generate stabilized views of all feature points. Experimental results demonstrate the effectiveness and robustness of the proposed video stabilization method on a variety of challenging videos.

Event cameras are promising sensors that show many advantages over frame-based cameras. Unlike conventional cameras, whose pixels share a common exposure time, event-based cameras represent a novel bio-inspired technology capable of capturing scenes with a high dynamic range and without motion blur. Due to their working principle, an event is generated when a pixel's brightness changes. Therefore, no event data is generated in a scenario where there is no relative motion between the event camera and the scene. However, in this study, we present a new method to enable event generation with a static event camera on a scene with static objects, aiming to eliminate the requirement of relative motion between event camera and the scene. By projecting custom designed grayscale pattern sequences onto static scenes, we successfully triggered a controlled event generation without requiring camera or object motion. Instead of a direct black-to-white transition, we used a sequence of contrast compatible grayscale projection pattern to regulate event rates and prevent bandwidth overload. To prevent event loss over time, we equalized all timestamps to the first timestamp. Since events in event cameras gradually lose their impact and reset over time, this adjustment prevents the decay of event information and ensures a continuous and stable event generation. Despite the absence of motion, we achieved reasonable results in image quality metrics such as MSE, LPIPS, SSIM. In this way, we aim to expand the usage areas of event cameras and make significant progress in data collection processes, especially for static camera and scenes.

Video Steganography provides a secure way of communication by embedding the secret message inside video frames. Supported by a real-time environment, a significant growth in choosing video as a cover file has been noticed in the last decade. Large concealing capacity, more robustness to attacks, and indistinguishability of cover video and stego video are of major concern for any video steganography technique. In this paper, a novel technique has been proposed to achieve the above-mentioned objectives. Implementation of video steganography in the transform domain inherently contributes to improved robustness. We have embedded an image inside a chosen video file using the Lifting Scheme based on the Haar Wavelet Transform. To achieve increased concealing capacity, the Singular Value Decomposition technique has been applied before the actual embedding task. The proposed work is implemented using the MATLAB environment. The suggested method is then tested and analyzed for its robustness, capacity, and imperceptibility. It is concluded that proposed technique is robust against general video processing attacks, like low-pass filtering, scaling, rotation, transformation, and histogram equalization attacks. Additionally, the values for Peak Signal to Noise Ratio, Mean Square Error, Structure Similarity Index Matrix, and Correlation Coefficients were calculated and analyzed as a performance measure of the method.

The widespread diffusion of hand-held devices with video recording capabilities requires the adoption of reliable digital Stabilization methods to enjoy the acquired sequences without disturbing jerkiness. In order to effectively get rid of the unwanted camera movements, an estimate of the global motion between adjacent frames is necessary. This paper presents a novel approach for estimating the global motion between frames using a curve warping technique known as dynamic time warping. The proposed algorithm guarantees robustness also in presence of sharp illumination changes and moving objects.

Change detection with background subtraction process remains to be an unresolved issue and attracts research interest due to challenges encountered on static and dynamic scenes. The key challenge is about how to update dynamically changing backgrounds from frames with an adaptive and self-regulated feedback mechanism. In order to achieve this, we present an effective change detection algorithm for pixelwise changes. A sliding window approach combined with dynamic control of update parameters is introduced for updating background frames, which we called sliding window-based change detection. Comprehensive experiments on related test videos show that the integrated algorithm yields good objective and subjective performance by overcoming illumination variations, camera jitters, and intermittent object motions. It is argued that the obtained method makes a fair alternative in most types of foreground extraction scenarios; unlike case-specific methods, which normally fail for their nonconsidered scenarios.

This paper presents a fast image sequence stabilization system that uses one bit transform (IBT) matching based global motion estimation and Kalman filtering based global motion correction. In order to facilitate a fast motion estimation process, image frames are initially converted into a single bit-plane representation using the IBT, and then motion vectors are evaluated matching the IBTs of two consecutive frames using Boolean operations. Because a binary representation is established, the computational complexity of motion estimation is significantly reduced providing a proportionate reduction in power consumption of custom hardware. The global motion vector is computed using local motion vectors of sub-image blocks to further increase the motion estimation speed. For the motion correction part, global motion vectors are accumulated to yield absolute frame displacements and Kalman filtering is employed to achieve stabilization.

Undesired vibration in videos is more and more common due to the rise of hand-held or vehicle mounted cameras. Non-indented motion of a video capturing device causes an unpleasant vibrating effect for the consumer. Therefore, video stabilization has been an extremely active field of research in the past years. Motion estimation is the most computationally expensive step of the video stabilization process. Our goal is to circumvent this expensive step in order to achieve real-time performance. We do so by using the precomputed motion vectors from the encoded video streams and thus operate in the compressed domain. These vectors already contain an approximation of the needed motion information. A low-level motion model is used for mitigating complexity, and a low-pass filter performs motion smoothing before the final motion compensation step which is used for correcting the video. In many real-time applications where the video vibration is moderate, the proposed framework can reach online video stabilization at 30 frames per second for high definition video and 60 frames per second for lower resolutions, while retaining satisfactory performance in video correction, comparable to pixel-based equivalent algorithms.

Traditional image-based mosaicing deals with the problem of parallax by imposing constraints of a parallax-free camera configuration or requiring a dense sampling of the scene. These solutions are often impractical or fail to address the needs of the application. Instead, taking advantage of depth cues and a criterion of smooth transitions, we achieve significantly improved mosaicing results for static scenes, coping effectively with non-trivial parallax in the input. Furthermore, by incorporating a criterion of consistent motion perception, we demonstrate progress on mosaicing of dynamic scenes without introducing artifacts. Although further additions are required to cope with unconstrained object motion, our algorithm can synthesize perceptually convincing dynamic mosaics, conveying the same appearance of object motion as seen in the original sequences.

Application of Self2Self self-supervised denoising framework to airborne gamma-ray spectrometry data.

The rapid accumulation of marine debris poses a substantial threat to ocean ecosystems, specifically the degradation of plastics, composite materials, and metals. Effective detection and classification of debris by material type are essential during the waste management and material recycling process. However, the underwater debris detection is often hampered by low contrast, color distortion, and noise in underwater imagery. Different deep learning models are proposed in the literature for debris detection, however most of them suffered with limitations in real time implementation complexity and inaccurate instance segmentation. This work proposes an adaptive hybrid lightweight Mask R-CNN system including image augmentation, object identification, and real-time instance segmentation to handle these issues. The preprocessed images are feed to the lightweight mask RCNN model for the object detection and segmentation. The proposed model uses an upgraded Region Proposal Network (RPN) for exact localization of underwater trash and MobileNetV3 as a lightweight backbone for effective feature representation. The model uses data augmentation methods including contrast correction, flipping, and blurring to improve robustness; the model also trained on the proprietary underwater debris datasets. Compared to the conventional approaches, performance evaluation metrics employing Mean Average Precision (mAP), Structural Similarity Index Measure (SSIM), Intersection over Union (IoU), and Peak Signal to Noise Ratio (PSNR) shows better accuracy. Furthermore, the model performs real-time computing at 30 FPS, which makes it highly suitable for usage in real-time operations.

The object of research is the adaptive switching weighted median image filter (ASWM) algorithm. This algorithm is one of the most effective in the field of impulse noise suppression. The computational complexity and algorithmic features of this adaptive nonlinear filter make it impossible to implement a filter that works in real time on modern PLD microcircuits. The most problematic areas of the algorithm are the weight coefficient estimation cycle, which has no limit on the number of iterations and contains a large number of division operations. This does not allow implementing the filter on PLDs with a sufficiently effective method. In the course of the research, the programming model of the filter in Python was used. The performance of the algorithm was assessed using the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) metrics. Modeling made it possible to find out empirically the number of iterations of the cycle for estimating the weight coefficients at different levels of noise density and to estimate the effect of artificial limitation of the maximum number of iterations on the filter performance. Regardless of the intensity of the noise impact, the algorithm performs less than 40 iterations of the evaluation cycle. Let’s also simulate the operation of the algorithm with different variants of the division module implementation. The paper considers the main of them and offers the most optimal in terms of the ratio of accuracy/hardware costs for implementation. Thus, a modified algorithm was proposed that does not have these disadvantages. Thanks to modifications of the algorithm, it is possible to implement a pipelined ASWM image filter on modern PLDs. The filter is synthesized for the main families of Intel PLDs. The implementation, which is not inferior in terms of SSIM and PSNR metrics to the original algorithm, requires less than 65,000 FPGA logical cells and allows filtering of monochrome images with FullHD resolution at 48 frames/s at a clock frequency of 100 MHz.

Video Stabilization For Vehicular Applications Is An Important Method Of Removing Unwanted Shaky Motions From Unstable Videos. In This Paper, An Improved Video Stabilization Method With Image Stitching Has Been Proposed. Scale Invariant Feature Transform (Sift) Matching Is Used To Calculate The New Position Of The Points In Next Frame. Image Stitching Is Done In Every Frame To Get Stabilized Frames To Provide Stable Video As Well As A Better Understanding Of The Previous Frame’S Position And Show The Surrounding Objects Together. The Computational Complexity Of Sift (Scale-Invariant Feature Transform) Is Reduced By Reducing The Sift Descriptors Size And Resticting The Number Of Keypints To Be Extracted. Also, A Modified Matching Procedure Is Proposed To Improve The Accuracy Of The Stabilization.