Tracking vehicles using a network of cameras with non-overlapping views is a challenging problem of great importance in traffic surveillance. One of the main challenges is accurate vehicle matching across the cameras. Even if the cameras have similar views on vehicles, vehicle matching remains a difficult task due to changes of their appearance between observations, and inaccurate detections and occlusions, which often occur in real scenarios. To be executed on smart cameras the matching has also to be efficient in terms of needed data and computations. To address these challenges we present a low complexity method for vehicle matching robust against appearance changes and inaccuracies in vehicle detection. We efficiently represent vehicle appearances using signature vectors composed of Radon transform like projections of the vehicle images and compare them in a coarse-to-fine fashion using a simple combination of 1-D correlations. To deal with appearance changes we include multiple observations in each vehicle appearance model. These observations are automatically collected along the vehicle trajectory. The proposed signature vectors can be calculated in low-complexity smart cameras, by a simple scan-line algorithm of the camera software itself, and transmitted to the other smart cameras or to the central server. Extensive experiments based on real traffic surveillance videos recorded in a tunnel validate our approach.
