image quality for recognition tasks in the automotive environment anthony winterlich vladimir...

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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0.9

1

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

0.1

0.2

0.3

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0.5

0.6

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Compression Ratio

FP

PF

JPEG Compression

FPPF vs. Compression RatioPower Fit

20253035400

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0.2

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0.5

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0.9

Signal to Noise Ratio (dB)

Det

ecti

on R

ate

Gaussian Noise

Detection Rate vs. NoiseSigmoid Fit

152025303540450

0.01

0.02

0.03

0.04

0.05

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!