[Computer Vision] Visual Understanding of Traffic Scenes

[Computer Vision] Visual Understanding of Traffic Scenes

Job Number: P20INT-37
​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.
San Jose, CA

You are expected to: 

  • Capturing the semantics of visual scenes by explicit modeling of objects, their attributes, and relationships to other objects and the environment.
  • Higher-level classification/recognition of dynamic traffic scenes including place, conditions, and spatial relationships using temporal event detection, action recognition, and localization.
  • Detection and understanding of unstructured events that impact navigation, such as disabled vehicles, construction zones, traffic accidents, etc.
  • Develop and evaluate metrics to verify reliability of the proposed algorithms.
  • Contribute to a portfolio of patents, academic publications, and prototypes to demonstrate research value.

Qualifications:

  • Ph.D. or highly qualified 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: scene graphs, spatio-temporal graphs, graph neural networks, visual recognition, video classification.
  • Experience in open-source deep learning frameworks such as TensorFlow or PyTorch preferred
  • Excellent programming skills in Python or C++.

Duration: 3 months

How to apply

Candidates must have the legal right to work in the U.S.A.​ Please add Cover Letter and CV in the same document

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