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Let's Go Bananas: Beyond Bounding Box Representations for Fisheye Camera-Based Object Detection in Autonomous

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Summary
This summary is machine-generated.

Object detection for fisheye cameras is challenging due to distortion. This study introduces a novel curved box representation, significantly improving accuracy for near-field surround-view sensing in autonomous driving.

Keywords:
automated drivingfisheye camerasobject detectionsurround view cameras

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Area of Science:

  • Computer Vision
  • Robotics
  • Autonomous Driving

Background:

  • Object detection is crucial for autonomous driving, with pedestrian detection being a well-established area.
  • Fisheye cameras offer wide-angle, near-field sensing but present unique challenges for standard object detection methods due to severe radial distortion.

Purpose of the Study:

  • To explore and develop effective object representations for fisheye cameras in autonomous driving.
  • To address the limitations of traditional bounding boxes in distorted fisheye imagery.

Main Methods:

  • Implemented a YOLO (You Only Look Once) based framework to evaluate object representations on the WoodScape dataset.
  • Investigated standard bounding boxes, oriented bounding boxes, ellipses, generic polygons, and proposed novel curvature-adaptive polygons and curved boxes.
  • Incorporated vanishing-point constraints and a camera geometry tensor for improved distortion adaptation.

Main Results:

  • The curvature-adaptive polygon improved mean average precision (mAP) by 3 points over standard bounding boxes.
  • The proposed curved box representation, enhanced with vanishing-point constraints, outperformed standard bounding boxes by 3 mAP and oriented bounding boxes by 1.6 mAP.
  • Further improvements of 1.4 mAP were achieved using a camera geometry tensor for non-linear distortion adaptation.

Conclusions:

  • Traditional bounding boxes are inadequate for fisheye cameras due to significant distortion.
  • Novel curved box representations offer a practical and accurate solution for object detection in fisheye images.
  • The developed methods enhance the reliability of near-field sensing for autonomous vehicles.