Risks of life on the street caused by inclement weather, harassment, and assault threaten the unsheltered homeless population. We address some challenges of enumerating the street homeless population by testing a novel capture-recapture (CR) estimation approach that models individuals' intermittent daytime visibility. We tested walking and vehicle-based variants of CR in downtown Toronto in March. Estimates that assume individual variability of sighting probabilities are most consistent with our knowledge of the homeless and achieve the most favorable confidence intervals, estimated detection probabilities, and coefficient of variation. Estimation bias from interobserver discrepancies, duplicate counting, and violation of the closed population assumption were minimized with uniform identification criteria, training, and sampling design. Bias caused by the social grouping of the homeless was small. Despite the limitations of visual identification, CR approaches as part of a multiple-method program can aid community responses to immediate needs on the street, especially during the harsh winter months.