Cellular traffic is a central aspect of cell function in health and disease. It is highly dynamic, and can be investigated at increasingly finer temporal and spatial resolution due to new imaging techniques and probes. Manual tracking of these data is labor-intensive and observer-biased and existing automation is only semi-automatic and requires near-perfect object detection and high-contrast images. Here, we describe a novel automated technique for quantifying cellular traffic. Using local intrinsic information from adjacent images in a sequence and a model for object characteristics, our approach detects and tracks multiple objects in living cells via Multiple Hypothesis Tracking and handles several confounds (merge/split, birth/death, and clutters), as reliable as expert observers. By replacing the related component (e.g. using a different appearance model) the method can be easily adapted for quantitative analysis of other biological samples.