Authors - Rohini Hongal, Supriya K, Rajeshwari .M, Rahil Sanadi Abstract - Computer vision applications like object detection, picture matching, 3D reconstruction, and depth estimation in navigation rely on the synchronization of stereo frames. In stereo vision, two cameras separated by known distance are used to capture an image and analyze for differences in both images. To use stereo images in any application, synchronization between the corresponding frames must be ensured. This paper presents an approach to detect the synchronization between the stereo pair images. The synchronization information between the stereo frames can be achieved in two ways: one is by using the temporal data of the image pair and the other is by analyzing the spatial data in the images. This study uses the temporal data i.e. timestamps of the stereo images and validates results with the spatial data, to identify the stereo image pair as synchronous or asynchronous. The spatial algorithm is executed once the timestamp algorithm identifies a possible synchronization. In order to generate a template and extract spatial information from the left frame, this technique makes use of the Sobel filter. An appropriate correlation approach is then used to match the template to the right, right+1, and right-1 frames. If the chosen frame matches the correct frame, the frames are deemed to be synchronized. The frame with the highest correlation is chosen. On the other hand, the frames are considered asynchronous, if the frame with the highest correlation is either the right+1 or right- 1 frame. The suggested approach offers an accuracy of 90.33for static datasets and 96.67frame synchronization. The technique also provides information on the duration of asynchrony when frames are not synchronized. A variety of computer vision applications that depend on synchronized stereo frames might benefit greatly from the presented technique. It allows for more reliable object detection, picture matching, and 3D reconstruction by precisely detecting the synchronization state, which improves visual perception and comprehension in real-world circumstances.
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