Superpixels Based Marker Tracking Vs. Hue Thresholding In Rodent Biomechanics Application

TL;DR

This paper compares superpixel-based marker tracking with hue thresholding for analyzing rodent locomotion, demonstrating that superpixels offer more reliable segmentation for biomechanical studies.

Contribution

It introduces and compares two automatic marker segmentation methods, highlighting the effectiveness of superpixel segmentation over hue thresholding in rodent biomechanics.

Findings

Superpixel segmentation outperforms hue thresholding in reliability.

Superpixels incorporate color and spatial information for better tracking.

The proposed methods facilitate large-scale, automatic analysis of rodent movement.

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. We propose two methods to segment and track these markers: first, using SLIC superpixels segmentation with a tracker based on position, speed, shape, and color information of the segmented region in the previous frame; second, using a thresholding on hue channel following up with the same tracker. The comparison showed that the SLIC superpixels method was superior because the segmentation was more reliable and based on both color and spatial information.