Vehicle 3d Localization in Road Scenes VIA a Monocular Moving Camera

Abstract

Knowing the 3D locations of the surrounding vehicles is of vital importance in autonomous driving scenarios. It can be pretty challenging to make an accurate estimation from a monocular moving camera. In this paper, we present an effective vehicle 3D localization method, that utilizes 2D keypoints predicted from a trained CNN to model the vehicles' structure, from which the ground points are further inferred. An adaptive ground plane estimation method is exploited under the monocular camera for 3D geometric back-projection. Benefiting from tracking, we also take into account temporal information of the same object to ensure the trajectory consistency. Viewpoint and size knowledge are also considered for refinement. The evaluation on the KITTI benchmark for on-road vehicles shows the effectiveness of our proposed approach with promising 3D localization results.

GLFER-Net: a polyphonic sound source localization and detection network based on global-local feature extraction and recalibration

“…Moreover, SSLD can collaborate with the speech enhancement task to denoise specific speakers by capturing their positions in intelligent meeting rooms [3]. It is also applied in smart cities for real-time environmental sound monitoring [4].…”

“…Fig 4. The overall structure of the multi-scale feature extraction (MSFE) module and the details of the cross-scale shuffle (CSS) unit therein…”