Video stabilization: A comprehensive survey

Self Cite

With the popularity of handheld devices, video stabilization is becoming increasingly important. In previous studies, many methods have been proposed to stabilize shaky videos. However, these methods fail to balance between image content integrity and stability. Some methods sacrifice image content for better stability. Other methods ignore the subtle jitters, which leads to poor stability. This work innovatively proposes a video stabilization method based on decomposed motion compensation. First, a grid‐based motion statistics method is adopted for motion estimation, which obtains more accurate motion vectors according to matched likelihood estimates. Then, the motion compensation is inherently decomposed into two parts: linear motion compensation and auxiliary motion compensation. Linear motion compensation removes complex jitter by constructing linear path constraints to obtain a more stable camera path. Auxiliary motion compensation uses a moving average filter to remove the high‐frequency jitter as a supplement and preserve more image content. The two components are combined with individual weights to derive the final transform matrix and warp the original frames. Experimental results show that our method outperforms the previous methods on NUS and DeepStab datasets qualitatively and quantitatively.

Section: Related Workmentioning

confidence: 99%

“…Traditional methods [1–12] usually consist of three main steps (see Figure 1): 1) Motion estimation, 2) Motion compensation, and 3) Image warp. The first step aims at estimating the global motion vectors.…”

Section: Introductionmentioning

confidence: 99%

DMCVS: Decomposed motion compensation‐based video stabilization

Huang,

Liu,

Jiang

et al. 2024

Self Cite

“…Convolutional Neural Networks (CNNs) are a widely used algorithm in the field of deep learning [36, 42] and have played a significant role in the remarkable achievements of this area. They are a type of feed‐forward neural network that utilizes convolutional computations to extract features from raw input data.…”

Section: Related Workmentioning

confidence: 99%

Ship detection based on YOLO algorithm for visible images

Huang,

Sun,

Wang

et al. 2023

Self Cite

Ship detection is a crucial task for waterway surveillance and channel optimization, especially in close proximity to the shore. However, detecting ship in visible image‐based detection remains a challenge due to the limited nature of visible image datasets. To address this issue, the Inland Ships Data Set (ISDS) is constructed to facilitate research on ship identification. On the other hand, most detection methods struggle to accurately identify ships that are small in size. Therefore, a visible image‐based ship detection model is proposed that employs a multi‐scale weighted feature fusion structure with the YOLOv4 detection model to improve the efficacy of small ship detection. Specifically, the YOLOv4 model is improved through fusing multi‐scale feature, redesigning priori frame, and enhancing loss function. The model, named YOLOv4‐MSW (i.e. YOLOv4 based on Multi‐Scale Weighted feature fusion), exhibits improved performance on ship detection in experiments conducted on the ISDS dataset, outperforming the original YOLOv4 model by improving the average precision (AP) by 4.87% and the recall rate by 10.03%. Meanwhile, the model achieve better detection accuracy and improve the average precision rate by at least 0.86% compared to existing learned object detection methods. The code related to this work are released at https://github.com/Sunhuashan/YOLOv4‐MSW. The whole dataset is available at https://drive.google.com/drive/folders/1fzJ2fcqiko6lFwqIEGghMceoQgv‐8jBy.

“…The current image matching has a wide range of application scenarios, as shown in Figure 2, including image retrieval [1,2], 3D reconstruction [3,4], simultaneous localization and mapping (SLAM) [5], video stabilization [6,7], etc. Taking 3D reconstruction as an example, in the field of service robotics research, the aim is to enable robots to autonomously perform movements, avoid obstacles, recognise objects and interact with behaviours in the environment.…”

Section: Introductionmentioning

confidence: 99%

A survey of feature matching methods

Huang,

Guo,

Wang

et al. 2024

Self Cite

Feature matching plays a crucial role in computer vision, with applications in visual localization, simultaneous localization and mapping (SLAM), image stitching, and more. It establishes correspondences between sets of feature points from multiple images, enabling various tasks. Over the years, feature matching has witnessed significant development, with an increasing number of methods being applied. However, different methods exhibit different degrees of applicability in different scenarios and requirements due to their different rationales. To cope with these issues, a comprehensive analysis and comparison of matching methods are essential. Existing reviews often lack coverage of deep learning models and focus more on feature detection and description, neglecting the matching process. This survey investigates feature detection, description, and matching techniques within the feature‐based image‐matching pipeline. Representative methods, their mechanisms, and application scenarios are also briefly introduced. In addition, comprehensive evaluations of classical and state‐of‐the‐art methods are conducted through extensive experiments on representative datasets. Particularly, matching‐based applications are compared to fully demonstrate the advantages of the methods. Lastly, this survey highlights current problems and development directions in matching methods, serving as a reference for researchers in the field.