Mask-KLT: Sub-pixel Accurate Directional Motion Estimation by Stripe Masking

Abstract

To diagnose the safety or monitor the health of aging infrastructures and factory facilities, a way to measure precise vibration or displacement is required. In this paper, we present a sub-pixel accurate tracking method of directional motion of interest, which we name Mask-KLT by inheriting the long-standing Kanade-Lucas-Tomasi tracker. Our simple method can estimate directional displacements of an object of interest along any single axis and is also robust to non-corner regions lacking good features to track. Our algorithm can be easily implemented by simple modification of the KLT tracker: by pre-processing an input image with a stripe pattern masking. We evaluate our proposed method with sub-pixel accuracy on a synthetic dataset. Our Mask-KLT dramatically increases the success rate of tracking and decreases displacement errors compared to Vanilla KLT. Moreover, we demonstrate the effectiveness of our method in a real-world experimental setup: A rotating machine in a laboratory environment. © 2023 IEEE.