Object tracking by combining detection, motion estimation and verification

摘要

Object detection and tracking play an increasing role in modern surveillance systems. Vision research is still confronted with many challenges when it comes to robust tracking in realistic imaging scenarios. We describe a tracking framework which is aimed at the detection and tracking of objects in real-world situations (e.g. from surveillance cameras) and in real-time. Although the current system is used for pedestrian tracking only, it can easily be adapted to other detector types and object classes. The proposed tracker combines i) a simple background model to speed up all following computations, ii) a fast object detector realized with a cascaded HOG detector, iii) motion estimation with a KLT Tracker iv) object verification based on texture/color analysis by means of DCT coefficients and , v) dynamic trajectory and object management. The tracker has been successfully applied in indoor and outdoor scenarios it a public transportation hub in the City of Graz, Austria.