An Efficient Filtering Technique for Detecting Vehicle Traffic in Real-Time Videos

Filtering strategies are frequently used in instantaneous video processes, particularly for applications such as identifying items for traffic recordings, to increase the standard of the footage frames and the precision of object recognition processes. Several varieties of filters can be employed for this, including the Kalman filter, mean filter, and Wiener filter. Key photographic metrics involving PSNR (Peak Signal-to-Noise Ratio), SSIM (Structural Similarity Index), and MSE (Mean Squared Error) are used to evaluate how well these filters work. According to findings from experiments, the Kalman filter operates better based on PSNR, SSIM, and MSE numbers than conventional mean and Wiener filters. Improved visual clarity is the result of the Kalman filter’s greater decrease in noise skills and preservation of the underlying structure of the film’s pixels. As a result, the precision of current traffic object recognition systems is greatly improved by these excellent frames. Applying the Kalman filter produced noticeably better outcomes for each studied output parameter producing MSE of 0.000123, PSNR of 42.35 and SSIM of 0.998 respectively. The tool used for execution is python.