A Multi-Frame Particle Tracking Algorithm Robust Against Input Noise

The performance of a particle tracking algorithm which detects particle trajectories from discretely recorded particle positions could be substantially hindered by the input noise. In this paper, a particle tracking algorithm is developed which is robust against input noise. This algorithm employs the regression method instead of the extrapolation method usually employed by existing algorithms to predict future particle positions. If a trajectory cannot be linked to a particle at a frame, the algorithm can still proceed by trying to find a candidate at the next frame. The connectivity of tracked trajectories is inspected to remove the false ones. The algorithm is validated with synthetic data. The result shows that the algorithm is superior to traditional algorithms in the aspect of tracking long trajectories.