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Image Quality for Recognition tasks in the Automotive Environment

Image Quality for Recognition tasks in the Automotive Environment. Anthony Winterlich Vladimir Zlokolica Edward Jones Martin Glavin Connaught Automotive Research Group Electrical & Electronic Engineering National University of Ireland, Galway. Current Applications for object detection.

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Image Quality for Recognition tasks in the Automotive Environment

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  1. Image Quality for Recognition tasks in the Automotive Environment Anthony Winterlich Vladimir Zlokolica Edward Jones Martin Glavin Connaught Automotive Research Group Electrical & Electronic Engineering National University of Ireland, Galway

  2. Current Applications for object detection

  3. Current Applications for object detection

  4. Object Detection & 3D depth modelling • Feature Detection • Motion Vector Field

  5. Object Detection & 3D depth modelling • HDR/Contrast • Noise • Sharpness

  6. Radial distortion

  7. Objective Image Quality Metrics Daimler Mono Ped. Detection Benchmark dataset CVC Dataset: Computer Vision Center, Autonomous University of Barcelona PennFudan Dataset PNG format PGM format PNG format 580x516 = 876KB 640x480 VGA = 300KB 640x480 x3 = 900KB

  8. Objective Image Quality Metrics The Pearson correlation coefficients of metric score to detection rates SSIM performs reasonably well across all distortion types

  9. Reference image HOG features of reference compression noise

  10. An Oriented Gradient based Image Quality Metric for Pedestrian Detection Performance Evaluation A “lost edge” due to noise corruption. An incorrectly detected edge due to a loss of high frequency components.

  11. Research Goal • Image Quality Metric for motion tracking/feature detection for automotive images.

  12. Thank You!

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