This study presents a robust single particle tracking (SPT) algorithm for live cell time-lapse sequences, addressing key challenges such as high particle density, motion heterogeneity, temporary disappearance, and merging/splitting events. The algorithm uses the linear assignment problem (LAP) framework to globally optimize particle tracking, enabling accurate and complete tracking of dense particle fields. It first links particles between consecutive frames and then links track segments to close gaps and capture merging and splitting events. This approach provides a computationally feasible approximation to the theoretically optimal multiple-hypothesis tracking (MHT) method, which is computationally prohibitive for large datasets. The algorithm was validated on simulated tracks with varying densities and signal stability, demonstrating its effectiveness in tracking under high-density conditions. It was applied to two key biological applications: (1) analyzing the lifetime of clathrin-coated pits (CCPs) in BSC1 cells, revealing that dynamin differentially affects the kinetics of long and short-lived CCPs; and (2) tracking CD36 receptors, showing that their aggregation probability increases along cytoskeleton-mediated linear tracks. The study highlights the importance of robust and complete tracking for understanding receptor organization at the plasma membrane level. The algorithm's ability to distinguish between linear and random motion was demonstrated by showing that CD36 receptors moving along linear tracks aggregated and dissociated more frequently than those moving randomly. The results indicate that motion type significantly influences receptor behavior, and the algorithm's global optimization approach is essential for capturing these dynamics accurately. The algorithm is versatile and applicable to a wide range of live cell imaging tasks, including cell motility, chromosome motion, synaptic vesicles, and cytoskeleton dynamics. It provides a powerful tool for studying subcellular dynamics in living cells, combining robust SPT with molecular interventions to uncover the molecular mechanisms underlying cellular organization.This study presents a robust single particle tracking (SPT) algorithm for live cell time-lapse sequences, addressing key challenges such as high particle density, motion heterogeneity, temporary disappearance, and merging/splitting events. The algorithm uses the linear assignment problem (LAP) framework to globally optimize particle tracking, enabling accurate and complete tracking of dense particle fields. It first links particles between consecutive frames and then links track segments to close gaps and capture merging and splitting events. This approach provides a computationally feasible approximation to the theoretically optimal multiple-hypothesis tracking (MHT) method, which is computationally prohibitive for large datasets. The algorithm was validated on simulated tracks with varying densities and signal stability, demonstrating its effectiveness in tracking under high-density conditions. It was applied to two key biological applications: (1) analyzing the lifetime of clathrin-coated pits (CCPs) in BSC1 cells, revealing that dynamin differentially affects the kinetics of long and short-lived CCPs; and (2) tracking CD36 receptors, showing that their aggregation probability increases along cytoskeleton-mediated linear tracks. The study highlights the importance of robust and complete tracking for understanding receptor organization at the plasma membrane level. The algorithm's ability to distinguish between linear and random motion was demonstrated by showing that CD36 receptors moving along linear tracks aggregated and dissociated more frequently than those moving randomly. The results indicate that motion type significantly influences receptor behavior, and the algorithm's global optimization approach is essential for capturing these dynamics accurately. The algorithm is versatile and applicable to a wide range of live cell imaging tasks, including cell motility, chromosome motion, synaptic vesicles, and cytoskeleton dynamics. It provides a powerful tool for studying subcellular dynamics in living cells, combining robust SPT with molecular interventions to uncover the molecular mechanisms underlying cellular organization.