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Abstract
Motion analysis and studying the motion patterns of objects play a crucial role in a variety of applications. Object tracking, as a means for motion analysis, is an integral part of new technologies like aircraft and satellite navigation or biomedical applications. It entails locating objects in a video, registering them over time, and extracting their trajectories for downstream analysis. In biomedical applications, the target objects could be cells, sub-cellular organelles, pathogens, or multi-cellular organisms. Some examples of Subsequent downstream analysis in the biomedical application could be classifying healthy cellular behavior from infected ones, differentiating motion of an organism with and without the presence of certain drugs for drug effectiveness studies, or identifying dynamic changes in cellular morphology as a result of the pathogenic invasion. The first and most important step in this process is the segmentation and tracking of relevant objects or the identification of regions in images corresponding to relevant biological phenomena and tracking those phenomena over time. Current tracking and segmentation approaches in biological applications have numerous drawbacks that severely limit their utility. First, they often make strong \textit{a priori} assumptions about the data, limiting the efficacy of the approach to a narrow subset of biological/biomedical applications contexts specifically. Second, most state-of-the-art methods require a large amount of “ground truth”, manually annotated image data, to accurately train their models. This is a significant obstacle for tracking objects in different settings. Third, most modern approaches are designed to track no more than single objects, while in biological/biomedical applications, we need to analyze the motion dynamics of hundreds of objects simultaneously, with possible occlusions and deformations, to study the change in motion behavior. Here, we propose a scalable unsupervised biomedical motion tracking framework to addressing these shortcomings.