Finding available parking spaces in dense urban areas is a globally recognized issue in urban mobility. Whereas prior studies have focused on outdoor/street parking, we target at (indoor) parking garages where the infrastructure supports (e.g., GPS and Wi-Fi) assumed by existing proposals are unavailable and counting vehicles by crowdsensing is difficult. To this end, we present ParkGauge as a system gauging the congestion level of parking garages; it infers (coarse-grained) parking occupancy from crowdsensed parking characteristics instead of counting the parked vehicles. ParkGauge adopts mostly low-power sensors in the driver's smartphone to determine driving states, contexts and temporal parking characteristics of a garage, including time-to-park and time-in-cruising/queuing. Mining such data collected from a crowd of drivers at various garages yields a good measure of their congestion levels and provide recommendations (in real-time) to drivers coming to these venues.