Visual Tracking for Abrupt Motions of Human Sperm Using Smoothing Stochastic Approximate Monte Carlo

Abstract

Characteristic of sperm movement or sperm motility is one of important quality of sperm. Computer-aided Sperm Analysis (CASA) systems have attempted to give more accurate information about sperm motility. For collecting a single good sperm in Intracytoplasmic Sperm Injection (ICSI) procedures, achieving correct sperm trajectory is necessary. On the other hand, visual tracking of a sperm is a challenging issue because: (i) sperms have similar size and shape, (ii) a good sperm moves fast and has unpredictable motions. Furthermore, our sperm videos were taken by regular cameras which have low frame rate (about 20 fps), which cause the sperm-motion more abrupt. To address this problem, we propose searching driven stochastic sampling framework for visual tracking of abrupt motions. In here, searching algorithm will give promising regions of the target, and thus can replace the role of transition model in tracking system. For searching, we employ Coherency Sensitive Hashing (CSH) in a window to constrain search space in each frame, called as Search Window. And then for stochastic sampling framework, we adopt Smoothing Stochastic Approximate Monte Carlo (SSAMC) to specifically finding the target. The experimental results on human-sperm sequences show that: (i) comparing to our previous tracker which use geometric transition dynamic model, the proposed tracker can handle abrupt motions robustly, (ii) by using appropriate Search Window, the tracker can localize the sperm target and restrict observation of other similar sperms.