Threshold effect on particle tracking algorithms

Automated particle tracking algorithms are widely used by soft matter physicists as a research tool to detect and construct the trajectories of micron-sized particles in ffuids. Analyzing these trajectories will uncover the physics of the investigated particles mainly on the type of motion they undergo making them suitable for potential applications. A plethora of methods has been proposed and used for detection and tracking. In this work, we examine the performance of two commonly used tracking algorithms in terms of threshold dependencies in digital video images. One of them is the centroid method (CM), a well-known and used algorithm and the other is radial symmetry method (RSM) which is recently proposed. Here, we generate the synthetic digital video images consisting of randomly placed multiple particles and compare the absolute errors on the particle detection by varying threshold values. Our results suggest that both algorithms show dependence on the threshold value and on comparison RSM algorithm performs better than the CM algorithm when the noise level is zero. Moreover, the measured absolute errors show a strong dependence on threshold values when noise levels are increased (up to 20) especially for the RSM algorithm.