Abstract
Multiple Object Tracking (MOT) or Multiple Target Tracking (MTT) is an important computer vision task which focuses on detecting multiple targets of interests in videos or sequential images frames, recoding theirs identities, and maintaining their tracks. Multiple cell tracking in microscopy cell images is a typical MOT application, which is often confronted with problems such as noise, adhesion, and low contrast. Inspired by the natural ripple spreading phenomenon in liquid, this paper proposed a novel ripple spreading optimization (RSO) algorithm to detect and track multiple cells in low resolution and low contrast image sequences. Compared to other swarm intelligence algorithms, □ concepts such as ripple propagation, ripple decaying and ripple diffraction can be directly employed to image processing applications in designing searching statistics. Simulation results on three real world scenarios demonstrate the effectiveness of the method.
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