Multi-Target Tracking with Time-Varying Clutter Rate and Detection Profile: Application to Time-lapse Cell Microscopy Sequences
This addresses the challenge of reliable multi-target tracking for quantitative analysis in cell microscopy, which is incremental as it builds on existing Bayesian methods with adaptive parameter estimation.
The paper tackled the problem of tracking numerous similar cellular particles in noisy, time-lapse microscopy sequences with changing image characteristics, by proposing a Bayesian filtering framework that adaptively estimates clutter rates and detection probabilities, and showed it outperforms state-of-the-art trackers on synthetic and real data.
Quantitative analysis of the dynamics of tiny cellular and sub-cellular structures, known as particles, in time-lapse cell microscopy sequences requires the development of a reliable multi-target tracking method capable of tracking numerous similar targets in the presence of high levels of noise, high target density, complex motion patterns and intricate interactions. In this paper, we propose a framework for tracking these structures based on the random finite set Bayesian filtering framework. We focus on challenging biological applications where image characteristics such as noise and background intensity change during the acquisition process. Under these conditions, detection methods usually fail to detect all particles and are often followed by missed detections and many spurious measurements with unknown and time-varying rates. To deal with this, we propose a bootstrap filter composed of an estimator and a tracker. The estimator adaptively estimates the required meta parameters for the tracker such as clutter rate and the detection probability of the targets, while the tracker estimates the state of the targets. Our results show that the proposed approach can outperform state-of-the-art particle trackers on both synthetic and real data in this regime.