5/3/2023 0 Comments Autopano video fix parallax![]() This review is expected to assist transportation researchers and engineers with the selection of suitable CV techniques for video processing, and the usage of SSMs for various traffic safety research objectives. ![]() Finally, practical issues in traffic video processing and SSM-based safety analysis are discussed, and the available or potential solutions are provided. A detailed review of SSMs for vehicle trajectory data along with their application on traffic safety analysis is presented. Then, the video pre-processing and post-processing techniques for vehicle trajectory extraction are introduced. The CV algorithm that are used for vehicle detection and tracking from early approaches to the state-of-the-art models are summarized at a high level. With this aim in mind, this paper focuses on reviewing the applications of CV techniques in traffic safety modeling using SSM and suggesting the best way forward. However, as video processing and traffic safety modeling are two separate research domains and few research have focused on systematically bridging the gap between them, it is necessary to provide transportation researchers and practitioners with corresponding guidance. ![]() The application of Computer Vision (CV) techniques massively stimulates microscopic traffic safety analysis from the perspective of traffic conflicts and near misses, which is usually measured using Surrogate Safety Measures (SSM). We compared our approach with both the original algorithm and other existing methods and achieved significant improvements in eliminating stitching artifacts such as ghosting and discontinuities while maintaining the efficiency of fast algorithms. The experimental results demonstrate the performance of our proposed method through an improvement in image registration and fusion metrics. The seam and transition areas were fused using the fade-in and fade-out weighting algorithm to obtain smooth and high-quality stitched images. Then, the optimal seam algorithm was used in the image fusion stage to obtain the seam line and construct the fusion area. First, in the image registration stage, the gridded Binary Robust Invariant Scalable Keypoints (BRISK) method was used to improve the matching efficiency of feature points, and the Grid-based Motion Statistics (GMS) algorithm with a bidirectional rough matching method was used to improve the matching accuracy of feature points. Consequently, this study proposes a multi-channel image stitching approach based on fast image registration and fusion algorithms, which enhances the stitching effect on the basis of fast algorithms, thereby augmenting its potential for deployment in real-time applications. Fast image registration and fusion algorithms suffer from problems such as ghosting and dashed lines, resulting in suboptimal display effects on the stitching. The image registration and fusion process of image stitching algorithms entails significant computational costs, and the use of robust stitching algorithms with good performance is limited in real-time applications on PCs (personal computers) and embedded systems.
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