A major problem in metropolitan areas is finding parking spaces. Existing parking guidance systems often adopt fixed sensors or cameras that cannot provide information from the driver’s point of view. Motivated by the advent of dashboard cameras (dashcams), we develop neural-network-based methods for detecting vacant parking spaces in videos recorded by a dashcam. Detecting vacant parking spaces in dashcam videos enables early detection of spaces. Different from conventional object detection methods, we leverage the monotonicity of the detection confidence with respect to the distance away of the approaching target parking space and propose a new loss function, which can not only yield improved detection results but also enable early detection. To evaluate our detection method, we create a new large dataset containing 5,800 dashcam videos captured from 22 indoor and outdoor parking lots. To the best of our knowledge, this is the first and largest driver’s view video dataset that supports parking space detection and provides parking space occupancy annotations.