Multi-target tracking with time-varying clutter rate and detection profile: Application to time-lapse cell microscopy sequences

Seyed Hamid Rezatofighi*, Stephen Gould, Ba Tuong Vo, Ba Ngu Vo, Katarina Mele, Richard Hartley

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    63 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Article number7006807
    Pages (from-to)1336-1348
    Number of pages13
    JournalIEEE Transactions on Medical Imaging
    Volume34
    Issue number6
    DOIs
    Publication statusPublished - 1 Jun 2015

    Fingerprint

    Dive into the research topics of 'Multi-target tracking with time-varying clutter rate and detection profile: Application to time-lapse cell microscopy sequences'. Together they form a unique fingerprint.

    Cite this