Vehicle re-identification in tunnel scenes via synergistically cascade forests

Nowadays, numerous cameras have been equipped in tunnels for monitoring the tunnel safety, such as detecting fire, vehicle stopping, crashes, and so forth. Nevertheless, safety events in tunnels may occur in the blind zones not covered by the multi-camera monitoring systems. Therefore, this paper opens the challenging problem, tunnel vehicle re-identification (abbr. tunnel vehicle Re-ID), to make a between-camera speculation. Different from the open road scenes focused by existing vehicle Re-ID methods, tunnel vehicle Re-ID is more challenging because of poor light condition, low resolution, frequent occlusion, severe motion blur, high between-vehicle similarity, and so on. To be specific, we propose a synergistically cascade forests (SCF) model which aims to gradually construct the linking relation between vehicle samples with an increasing of alternative layers of random forest and extremely randomized forest. Through the modeling of SCF, we can restrict the influence of little inter-variation of different vehicle identities and large intra-variation of the same identities. This paper constructs a new and challenging tunnel vehicle dataset (Tunnel-VReID), consisting of 1000 pairs of tunnel vehicle images. Extensive experiments on our Tunnel-VReID demonstrate that the proposed method can outperform current state-of-the-art methods. Besides, in order to prove the adaptation ability of SCF, we also verify the superiority of SCF on a large-scale vehicle Re-ID dataset, named as VehicleID, collected in open road scenes.