Moving Object Detection Based on a Combination of Kalman Filter and Median Filtering

Abstract

The task of determining the distance from one object to another is one of the important tasks solved in robotics systems. Conventional algorithms rely on an iterative process of predicting distance estimates, which results in an increased computational burden. Algorithms used in robotic systems should require minimal time costs, as well as be resistant to the presence of noise. To solve these problems, the paper proposes an algorithm for Kalman combination filtering with a Goldschmidt divisor and a median filter. Software simulation showed an increase in the accuracy of predicting the estimate of the developed algorithm in comparison with the traditional filtering algorithm, as well as an increase in the speed of the algorithm. The results obtained can be effectively applied in various computer vision systems.

Herbivorous fish feeding dynamics and energy expenditure on a coral reef: Insights from stereo‐video and AI‐driven 3D tracking

“… Running Median Smoothing : We used the zoo package to apply a 5‐point running median filter (Zeileis et al., 2023). The choice of a 5‐frame filter size, given our dataset's 60 Hz acquisition rate, adeptly balances noise reduction and the preservation of intrinsic data features, all while achieving our targeted minimum resolution of 10 Hz. Kalman Filtering : Building upon the median‐smoothed data, we employed Kalman filtering, as suggested by Kalita and Lyakhov (2022). A Kalman filter is an algorithm that refines estimates of unknown variables over time using a series of measurements, even when these measurements contain noise or inaccuracies.…”

“…Kalman Filtering : Building upon the median‐smoothed data, we employed Kalman filtering, as suggested by Kalita and Lyakhov ( 2022 ). A Kalman filter is an algorithm that refines estimates of unknown variables over time using a series of measurements, even when these measurements contain noise or inaccuracies.…”

Energy-use strategies on a degraded coral reef: insights from remote underwater stereo-video and AI-driven 3D tracking

“…3. Kalman Filtering: Building upon the median-smoothed data, we employed Kalman ltering, as suggested by Kalita & Lyakhov (2022). This step was facilitated by the KFAS package, which hinges on the Gaussian distribution assumption of measurement noise (Helske 2017).…”