Efficient multi-camera vehicle detection, tracking, and identification in a tunnel surveillance application

Reyes Rios-Cabrera, Tinne Tuytelaars, Luc Van Gool
2012 Computer Vision and Image Understanding  
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 real-time 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).
more » ... 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 stages of the cascade. 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 minimal bandwidth. Finally, we show that the same subset of features can also be used effectively for tracking. This Haar-features based 'tracking-by-identification' yields surprisingly good results on standard datasets, without the need to update the model online. The general multi-camera framework is validated using three tunnel surveillance videos.
doi:10.1016/j.cviu.2012.02.006 fatcat:yib6ibeg5faqvficbeti2qyhsy