Robust visual rear ground clearance estimation and classification of a passenger vehicle

Computation of Visual Rear Ground Clearance of vehicles is an important computer vision application. This problem is challenging as the road and vehicle rear bumper may have subtle appearance differences, vehicle motion is on uneven surfaces and there are real-time considerations. In this paper a method is presented to compute the Visual Rear Ground Clearance of a vehicle from its rear view video and classify it into two classes namely Low Visual Rear Ground Clearance Vehicles and High Visual Rear Ground Clearance Vehicles. A multi-frame matching technique in conjunction with geometry based constraints is developed. It detects Regions-of-Interest ROIs of moving vehicles and moving shadows, and uses shape constraints associated with vehicle geometry as viewed from its rear. It tracks stable features on a vehicle to compute the Visual Rear Ground Clearance. The results are shown on a large dataset of videos and compared against the ground-truth.