In computer vision systems, stereo frames are used in various applications to better understand the sight being captured. To achieve this, proper synchronization is necessary between the two cameras collecting the image. Hence, detecting the synchronization between the stereo frames plays a vital role in the system developed to utilize the stereo frames. Synchronization is essential for accurate depth estimation and object tracking, as well as for other computer vision tasks that rely on temporal consistency. This study presents an approach to detect the synchronization of stereo frames. The primary objective of the proposed algorithm is to identify out-of-sync frames based on the timestamp data the images are captured. The algorithm compares the difference in time with a preset threshold. To validate the results obtained using timestamps, the Structural Similarity Index (SSIM) algorithm is used. SSIM is used to compare corresponding frames from the left and right cameras. By incorporating both the timestamp and SSIM analyses, the algorithm can provide more robust and accurate results in detecting synchronization among stereo frames. The ability to accurately detect and report mis-sync frames can greatly improve the reliability and effectiveness of such systems. The accuracy for this model is approximately 0.93233, which means it correctly classifies around 93.233% of the instances. The utilization of the Jetson AGX Orin Developer Kit, coupled with the VPI-based Gaussian filter showcased improved resource utilization in terms of memory consumption, CPU load, and GPU utilization. By utilizing VPI, the system effectively reduced memory usage while maintaining similar CPU utilization. Furthermore, the GPU’s computational capabilities were effectively leveraged when employing VPI, leading to a significant reduction in overall processing time.