This paper presents a method for detecting and tracking large number of arbitrary-oriented and densely aggregated microbes from image sequences captured under microscope. We first propose an integral channel feature (ICF)-based detector which is able to localize the dense and arbitrarily oriented targets with low false positive rate. Then instead of treating target detection and tracking as two separated problems as many previous works did, we propose to refine the detection results in the data association process. The kinematic pattern of microbes is well modeled with the proposed integral sliding energy (ISE), which is combined with detection response in a hybrid cost function. Minimizing the cost function allows us to simultaneously select the true targets from the detections and to match the targets across two consecutive frames. Systematical experiments have been conducted to demonstrate the effectiveness of proposed method.

本文提出了一种从显微镜下捕获的图像序列中检测和跟踪大量任意定向和密集聚集的微生物的方法。我们首先提出一种基于积分通道特征（ICF）的检测器，该检测器能够以低误报率定位密集且任意定向的目标。然后，我们提议将数据关联过程中的检测结果细化，而不是像许多以前的工作一样将目标检测和跟踪视为两个分离的问题。利用提出的积分滑动能（ISE）对微生物的运动模式进行了很好的建模，并与混合成本函数中的检测响应相结合。最小化成本函数使我们能够从检测中同时选择真实目标，并在两个连续的帧中匹配目标。