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 due to a common belief that parking garages are capable of delivering real-time occupancy information, we specifically target at (indoor) parking garages as this belief is far from true. This problem is very challenging as all the infrastructure supports (e.g., GPS and Wi-Fi) assumed by existing proposals are not available to parking garages, so counting how many vehicles are using a parking garage by crowd sensing can be extremely difficult. To this end, we present Park Gauge, a method to gauge the occupancy of parking garages, along with a reference system prototype for performance evaluation, it infers parking occupancy from crowd sensed parking characteristics instead of counting the parked vehicles. Park Gauge adopts low-power sensors (e.g., accelerometer and barometer) in the driver's smartphone to determine the driving states (e.g., turning and braking). A sequence of such states further allows the inference of driving contexts (e.g., driving, queuing and parked) that in turn yield temporal parking characteristics of a parking garage, including time-to-park and time-in-cruising/queuing. Mining such mobile data opportunistically collected from a crowd of drivers arriving at various garages yields a good measure of their occupancies and hence useful recommendations can be generated (in real-time) to inform drivers coming toward these venues. Through extensive experiments, we demonstrate that our method fully explores these parking characteristics to efficiently infer occupancies of parking garages with high accuracy.