This paper presents an integrated solution for the problem of detecting, tracking and identifying vehicles in a tunnel surveillance application, taking into account practical constraints including realtime operation, poor imaging conditions, and a decentralized architecture. Vehicles are followed through the tunnel by a network of non-overlapping cameras. They are detected and tracked in each camera and then identified, i.e. matched to any of the vehicles detected in the previous camera(s). To limit the computational load, we propose to reuse the same set of Haar-features for each of these steps. For the detection, we use an Adaboost cascade. Here we introduce a composite confidence score, integrating information from all stage of the cascades. A subset of the features used for detection is then selected, optimizing for the identification problem. This results in a compact binary `vehicle fingerprint', requiring very limited bandwidth. Finally, we show that the same set of features can also be used for tracking. This haar features based `tracking-by-identification' yields surprisingly good results on standard datasets, without the need to update the model online.