Vehicle detection and tracking at intersections by fusing multiple camera views

Abstract

Intersections are challenging locations for drivers. Complex situations are common due to the variety of road users and intersection layouts. This contribution describes a real time method for detecting and tracking vehicles at intersections using images captured by a static camera network. After background subtraction, the foreground segments are projected on a common fusion map. Using this fusion map, the pose, width, and height of the vehicles can be determined. After that, the detected objects are tracked by a Gaussian-Mixture approximation of the Probability Hypothesis Density filter. Results of the intersection perception can further be communicated to equipped vehicles by wireless communication.