3D Based Landmark Tracker Using Superpixels Based Segmentation for Neuroscience and Biomechanics Studies

TL;DR

This paper introduces a novel 3D landmark tracking method for neuroscience and biomechanics studies, combining superpixel segmentation, DLT projection, and Kalman filtering to accurately track markers in high-speed rodent locomotion videos.

Contribution

The paper presents a new automatic 3D marker segmentation and tracking approach using superpixels, DLT, and Kalman filters, improving accuracy and efficiency over manual methods.

Findings

Achieves 95% correct marker labeling

Effective in high-speed rodent locomotion videos

Handles various tracking difficulties

Abstract

Examining locomotion has improved our basic understanding of motor control and aided in treating motor impairment. Mice and rats are premier models of human disease and increasingly the model systems of choice for basic neuroscience. High frame rates (250 Hz) are needed to quantify the kinematics of these running rodents. Manual tracking, especially for multiple markers, becomes time-consuming and impossible for large sample sizes. Therefore, the need for automatic segmentation of these markers has grown in recent years. Here, we address this need by presenting a method to segment the markers using the SLIC superpixel method. The 2D coordinates on the image plane are projected to a 3D domain using direct linear transform (DLT) and a 3D Kalman filter has been used to predict the position of markers based on the speed and position of markers from the previous frames. Finally, a probabilistic function is used to find the best match among superpixels. The method is evaluated for different difficulties for tracking of the markers and it achieves 95% correct labeling of markers.