Computer Vision and Machine Learning in Traffic Scenes
Computer Vision and Machine Learning in Traffic Scenes (Job Number: P18INT-02)
Silicon Valley

The title includes multiple positions which focus on developing computer vision and machine learning algorithms to capture the detailed semantics of 2D and/or 3D traffic scenes. The candidate is expected to work on one of the following topics:

  • Segmentation, reconstruction, and interpretation through fusion of video and LiDAR point cloud data
  • Explicit modeling of objects, their attributes, and relationships to other objects and the environment
  • Higher level classification of dynamic traffic scenes including place, conditions, and environments
  • Representation and classification of atomic and complex actions of participants in traffic scenes for video captioning and retrieval
  • Vehicle attribute recognition for safe navigation including vehicle pose and turn signal status

 

Qualifications:

  • Ph.D. /M.S. candidate in computer science, electrical engineering, or related field
  • Strong familiarity with computer vision and machine learning techniques pertaining to scene understanding, image classification, and object detection
  • Hands-on experience in one or more of the following: LiDAR processing, SLAM, sensor fusion, video captioning and retrieval, probabilistic graphical modeling
  • Experience in open-source deep learning frameworks such as TensorFlow or PyTorch preferred
  • Excellent programming skills in Python or C++
  • Experience with Point Cloud Library (PCL), Robot Operating System (ROS), and GPU programming is a plus for some topics