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Pushing hard cases in tag detection with a CNN
Try to handle failures in april/aruco tag detection using deep learning.
Keywords: april aruco tag fiducial marker deep learning computer vision CNN
Visual Tags such as April or Aruco tags are nowadays detected with a handcrafted algorithm. This algorithm has its limitations in special cases, such as when the tag is far away from the camera, when the tag is partially occluded or when a camera with high distortion is used.
Visual Tags such as April or Aruco tags are nowadays detected with a handcrafted algorithm. This algorithm has its limitations in special cases, such as when the tag is far away from the camera, when the tag is partially occluded or when a camera with high distortion is used.
In this project, you will train a CNN to handle these special cases. We will first brainstorm a meaningful architecture that will allow a CNN to complement classical tag detection in the most effective way. You will then figure out the most effective way to create meaningful training data (hybrid of synthetic and real data?). Finally, you will use that data to train the desired detector.
In this project, you will train a CNN to handle these special cases. We will first brainstorm a meaningful architecture that will allow a CNN to complement classical tag detection in the most effective way. You will then figure out the most effective way to create meaningful training data (hybrid of synthetic and real data?). Finally, you will use that data to train the desired detector.
Titus Cieslewski ( titus at ifi.uzh.ch ), APPLY VIA EMAIL, ATTACH CV AND TRANSCRIPT! Required skills: Linux, Python, ability to read C++ code. Desirable skill: Tensorflow or similar.
Titus Cieslewski ( titus at ifi.uzh.ch ), APPLY VIA EMAIL, ATTACH CV AND TRANSCRIPT! Required skills: Linux, Python, ability to read C++ code. Desirable skill: Tensorflow or similar.