Vehicle tracking is of great importance for tunnel safety. To detect incidents or disturbances in traffic flow it is necessary to reliably track vehicles in real-time. The tracking is a challenging task due to poor lighting conditions in tunnels and frequent light reflections from tunnel walls, the road and the vehicles themselves. In this paper we propose a multi-clue tracking approach combining foreground blobs, optical flow of Shi-Tomasi features and image projection profiles in a Kalman filter with a constant velocity model. The main novelty of our approach lies in using vertical and horizontal image projection profiles (so-called vehicle signatures) as additional measurements to overcome the problems of inconsistent foreground and optical flow clues in cases of severe lighting changes. These signatures consist of Radon-transform like projections along each image column and row. We compare the signatures from two successive video frames to find their alignment and to correct predicted vehicle position and size. We tested our approach on several tunnel sequences. The results show an improvement in the accuracy of the tracker and less target losses when image projection clues are used. Furthermore, calculation and comparison of image projections is computationally efficient so the tracker keeps real-time performance (25 fps, on a single 1.86 GHz processor).