Vehicle global 6-DoF pose estimation under traffic surveillance camera

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Accurately sensing the global position and posture of vehicles in traffic surveillance videos is a challenging but valuable issue for future intelligent transportation systems. Although in recent years, deep learning has brought about major breakthroughs in the six degrees of freedom (6-DoF) pose estimation of objects from monocular images, accurate estimation of the geographic 6-DoF poses of vehicles using images from traffic surveillance cameras remains challenging. We present an architecture that computes continuous global 6-DoF poses throughout joint 2D landmark estimation and 3D pose reconstruction. The architecture infers the 6-DoF pose of a vehicle from the appearance of the image of the vehicle and 3D information. The architecture, which does not rely on intrinsic camera parameters, can be applied to all surveillance cameras by a pre-trained model. Also, with the help of 3D information from the point clouds and the 3D model itself, the architecture can predict landmarks with few and/or blurred textures. Moreover, because of the lack of public training datasets, we release a large-scale dataset, ADFSC, that contains 120 K groups of data with random viewing angles. Regarding both 2D and 3D metrics, our architecture outperforms existing state-of-the-art algorithms in vehicle 6-DoF estimation.

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ISPRS Journal of Photogrammetry and Remote Sensing

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