Cell detection and tracking are paramount for bio-analysis. Recent approaches
rely on the tracking-by-model evolution paradigm, which usually consists of
training end-to-end deep learning models to detect and track the cells on the
frames with promising results. However, such methods require extensive amounts
of annotated data, which is time-consuming to obtain and often requires
specialized annotators. This work proposes a new approach based on the
classical tracking-by-detection paradigm that alleviates the requirement of
annotated data. More precisely, it approximates the cell shapes as oriented
ellipses and then uses generic-purpose oriented object detectors to identify
the cells in each frame. We then rely on a global data association algorithm
that explores temporal cell similarity using probability distance metrics,
considering that the ellipses relate to two-dimensional Gaussian distributions.
Our results show that our method can achieve detection and tracking results
competitively with state-of-the-art techniques that require considerably more
extensive data annotation. Our code is available at:
this https URL

该研究论文提出了一种新的基于经典检测跟踪模式的细胞检测和跟踪方法，通过将细胞形状近似为有方向的椭圆并利用通用有方向对象检测器在每一帧中识别出细胞，再结合全局数据关联算法进行跟踪，结果表明该方法在细胞检测和跟踪方面与需要更多数据标注的最先进技术具有竞争力。