2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
Login Paper Search My Schedule Paper Index Help

My ICASSP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDIVMSP-31.4
Paper Title VEHICLE 3D LOCALIZATION IN ROAD SCENES VIA A MONOCULAR MOVING CAMERA
Authors Yanting Zhang, Donghua University, China; Aotian Zheng, University of Washington, United States; Ke Han, Fudan University, China; Yizhou Wang, University of Washington, United States; Jenq-Neng Hwang, University of Washinton, United States
SessionIVMSP-31: Applications 3
LocationGather.Town
Session Time:Friday, 11 June, 14:00 - 14:45
Presentation Time:Friday, 11 June, 14:00 - 14:45
Presentation Poster
Topic Image, Video, and Multidimensional Signal Processing: [IVARS] Image & Video Analysis, Synthesis, and Retrieval
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
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.