image quality for recognition tasks in the automotive environment anthony winterlich vladimir...
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Image Quality for Recognition tasks in the Automotive Environment
Anthony WinterlichVladimir ZlokolicaEdward Jones Martin Glavin
Connaught Automotive Research GroupElectrical & Electronic EngineeringNational University of Ireland, Galway
Current Applications for object detection
Current Applications for object detection
Object Detection & 3D depth modelling
Feature Detection
Motion Vector Field
Object Detection & 3D depth modelling
HDR/Contrast
Noise
Sharpness
Radial distortion
Objective Image Quality Metrics
PennFudan Dataset
PNG format
580x516 = 876KB
Daimler Mono Ped. Detection Benchmark dataset
PGM format
640x480 VGA = 300KB
CVC Dataset:Computer Vision Center,
Autonomous University of Barcelona
PNG format
640x480 x3 = 900KB
SSIM performs reasonably well across all distortion types
The Pearson correlation coefficients of metric score to detection rates
Objective Image Quality Metrics
0.0050.010.0150.020.0250.030.0350.040.0450.050.055
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Compression Ratio
Det
ecti
on R
ate
JPEG Compression
Detection Rate Vs Compression Ratio
Power Fit
0.0050.010.0150.020.0250.030.0350.040.0450.050.055
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0.2
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Compression Ratio
FP
PF
JPEG Compression
FPPF vs. Compression RatioPower Fit
20253035400
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Signal to Noise Ratio (dB)
Det
ecti
on R
ate
Gaussian Noise
Detection Rate vs. NoiseSigmoid Fit
152025303540450
0.01
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0.06Gaussian Noise
Signal to Noise Ratio (dB)
FP
PF
Reference image HOG features of reference
compression noise
A “lost edge” due to noise corruption.
An incorrectly detected edge due to a loss of high frequency components.
An Oriented Gradient based Image Quality Metric for Pedestrian Detection Performance Evaluation
Research Goal
Image Quality Metric for motion tracking/feature detection for automotive images.
Thank You!