Template-update-based visual tracking for automatic cell manipulation in FluidFM

Visual tracking is an essential process for performing automated cell manipulation tasks. However, Real-time accurate cell tracking in FluidFM is challenging due to probe occlusion during manipulation. This study presents a correlation filtering-based tracking method for real-time, high-precision position tracking of cells and probes. In this method, a target template update strategy is proposed to realize cell tracking under probe occlusion, which abandons the use of pixel values in the occlusion area and reduces the interference of the occlusion area in model learning. Cell tracking and automated manipulation experiments were carried out to validate the proposed method. The Experimental results show that the proposed tracking method can accurately measure the positions of occlusion cells and probe. Furthermore, combined with the precise force control of the FluidFM's probe, the automated cell Pick & Place experiments was accomplished with high accuracy and robustness.