Feature-aided particle tracking
We present a new feature-aided tracking algorithm dedicated to the task of tracking multiple and closely-spaced biological particles. We propose a new function to score associations, based on kinetic models, and enriched with an additional feature. This feature is based on adaptive profiles and the physical properties of the acquisition system. A key property is that this feature definition allows to resolve the challenging task of tracking particles that appear fused. Results on simulations show improved performances over existing methods both on tracking and on the resolution of fused particles.