Multi-camera vehicle identification in tunnel surveillance system

Abstract

Tunnel traffic security has received increasing attention since accidents in tunnels may cause serious casualties. Surveillance cameras are widely equipped in tunnels for traffic condition monitoring and safety maintenance. Vehicle identification among multiple cameras is an essential component in tunnel surveillance systems. In this paper, we propose a Spatiotemporal Successive Dynamic Programming (S2DP) algorithm for identifying vehicles between pairs of cameras. Taking color information into consideration, we extract features based on Harris corner detection with OpponentSIFT descriptors. "Tracking-by-identification " for vehicles across multiple cameras can thus be achieved. Extensive experiments on real tunnel video data show that the proposed S2DP algorithm outperforms state-of-the-art methods.

HAZMAT Vehicle Reidentification in Road Tunnels Based on the Fusion of Appearance and Spatiotemporal Information

“…The ReID algorithm applies the projection feature similarity of a radon transform between vehicle images. Chen et al [ 15 ] proposed a spatiotemporal successive dynamic programming algorithm to identify vehicles between pairs of cameras. They extracted features based on Harris corner detection and OpponentSIFT descriptors, considering color information [ 16 ].…”