A Dynamic Noise Correction Method for Person Recognition Using 3D Point Clouds According to Sprint Speed of Short Distance Runners

Accurately recognizing objects is crucial for ensur- ing the safety of automobiles and autonomous robots. However, fast movements, such as sprinting, cause motion blur, which complicates detection tasks. In this study, we developed an algorithm to enhance the accuracy with which 3D point clouds recognize individuals in sprinting motion. Data were collected using LiDAR sensors, which are commonly employed in au- tonomous driving technology. Our previous research explored methods for removing and correcting motion blur by adjusting the reference values for noise removal based on sprinting speed. Initial results showed that addressing dynamic noise signifi- cantly improved recognition accuracy compared to the original data. One challenge encountered during the prior investigation, however, was the excessive correction of lateral and vertical body movements. To tackle this issue, we proposed a refined method targeting motion blur specifically caused by sprinting. This method detects inter-frame blur in three dimensions. By comparing the proposed approach with the prior investigation’s results, we confirmed further improvements in recognition accuracy.