background subtraction for urban traffic monitoring using webcams master graduation project final...

Background Subtraction for Urban Traffic Monitoring

using Webcams

Master Graduation ProjectFinal Presentation

Supervisor: Rein van den Boomgaard

Mark Smids

December 12th 2006

Overview

• Introduction• Background Subtraction• Shadow Detection• Video Summarization • Demo’s

• Background Subtraction in action• Shadow Detector in action• Smart Surveillance using Video Summarization

• Evaluation • Conclusions

Introduction

• Introduction

• Background Subtraction

• Shadow Detection

• Video Summarization

• Demos

• Evaluation

• Conclusions

• Traditional ways of traffic monitoring

• using magnetic loops

Introduction

• Introduction

• Demos

• Evaluation

• Conclusions

• Limitations:

• These systems only count, very costly

Introduction

• Introduction

• Demos

• Evaluation

• Conclusions

• Limitations:

• For extended traffic monitoring we want to measure:

• road density, queue detection, vehicle speed, exact location of vehicles

Introduction

• Introduction

• Demos

• Evaluation

• Conclusions

• Limitations:

• For extended traffic monitoring we want to measure:

• road density, queue detection, vehicle speed, exact location of vehicles

• Solution: use cameras to monitor traffic automatically

Introduction

• Introduction

• Demos

• Evaluation

• Conclusions

• Why focus on an urban setting?

• Most research focused on a highway setting

• More challenging tasks

Introduction

• Introduction

• Demos

• Evaluation

• Conclusions

• Components of a vision based traffic monitoring system:

• cameras, calibration, background subtraction, tracking, shadow detection, parameter extraction, video summarization, …

Introduction

• Introduction

• Demos

• Evaluation

• Conclusions

• Components of a vision based traffic monitoring system:

• cameras, calibration, background subtraction, tracking, shadow detection, parameter extraction, video summarization, …

Background Subtraction

• Introduction

• Demos

• Evaluation

• Conclusions

• Deterministic approach

• Create an initial background model from the first

N frames

• For each new frame, subtract it from the background model to obtain a binary mask for all x,y: if I(x,y) – B(x,y) > T then M(x,y) = 1 else M(x,y) = 0

• Update the background model: for all x,y: if M(x,y) = 0 then B(x,y) = I(x,y)

• Introduction

• Demos

• Evaluation

• Conclusions

• Statistical approach

• Model each pixel in the background model by a mixture of Gaussians

),,(*)( ,,1

, titit

itit XXP

• Introduction

• Demos

• Evaluation

• Conclusions

• Statistical approach

• Model each pixel in the background model by a mixture of Gaussians

• How to determine those components that model

the background?• Observation: these Gaussians have the most supporting evidence and lowest variances • Order the K distributions in the mixture by the value of• The first B distributions are chosen as the background model, where:

minarg

),,(*)( ,,1

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itit XXP

Shadow Detection

• Introduction

• Demos

• Evaluation

• Conclusions

• Shadows: cast and self shadows

Shadow Detection

• Introduction

• Demos

• Evaluation

• Conclusions

• Elimination of cast shadows can improve background subtraction results very much…

Shadow Detection

• Introduction

• Demos

• Evaluation

• Conclusions

• Elimination of cast shadows can improve background subtraction results very much…

Shadow Detection

• Introduction

• Demos

• Evaluation

• Conclusions

• Consider the set of pixels classified as foreground pixels

Shadow Detection

• Introduction

• Demos

• Evaluation

• Conclusions

• A pixel is a candidate shadow pixel when the pixel value has a significant lower value

than it’s corresponding background value

Shadow Detection

• Introduction

• Demos

• Evaluation

• Conclusions

• Extend this idea: let c = (R,G,B) and

Rate of similarity:

),,( bgr

Shadow Detection

• Introduction

• Demos

• Evaluation

• Conclusions

• Extend this idea: let c = (R,G,B) and

Rate of similarity:

• If tau < < 1 then pixel is a shadow pixel

),,( bgr

Video Summarization

• Introduction

• Demos

• Evaluation

• Conclusions

• Application: smart vision based surveillance system

Video Summarization

• Introduction

• Demos

• Evaluation

• Conclusions

• Record only frames which includes relevant foreground objects

Video Summarization

• Introduction

• Demos

• Evaluation

• Conclusions

• Record only frames which includes relevant foreground objects

How to guarantee that a full trajectory of a vehicle is recorded?

• Introduction

• Demos

• Evaluation

• Conclusions

1. Shadow Detector in action – 1 | 2

2. Background Subtraction in action det 1 | stat 1 - det 2 | stat 2

3. Smart Surveillance using Video Sum. - 1

Evaluation

• Introduction

• Demos

• Evaluation

• Conclusions

• Test videos: three different weather conditions (5 minutes each)

• Goal: test both background subtraction algorithms on these videos

• Limitation: no ground truth available!

Evaluation

• Introduction

• Demos

• Evaluation

• Conclusions

• Evaluation on another level: using the video summarization component.

• A frame level ground truth is created

• For each algorithm a score can be computed

Evaluation

• Introduction

• Demos

• Evaluation

• Conclusions

100)1( N

• Introduction

• Demos

• Evaluation

• Conclusions

Score S

(deterministicapproach)

Score S

(statisticalapproach)

Totalnumber of Frames

Video A(wind/cloudy)

85.6% 88.3% 4581

Video B(sunny)

88.5% 94.6% 4163

Video C(rain)

83.4% 93.4% 3024

Evaluation

Conclusions

• For all weather conditions: the statistical approach outperforms the deterministic approach (5-10%)

• Introduction

• Demos

• Evaluation

• Conclusions

Conclusions

• Wind is the hardest problem from both algorithms

• Introduction

• Demos

• Evaluation

• Conclusions

Conclusions

• Statistical approach performs much better in the sunny settings

• Introduction

• Demos

• Evaluation

• Conclusions

Conclusions

• Statistical approach performs much better in the sunny settings

• Future work: create a pixel-level ground truth and evaluate both algorithms

• Introduction

• Demos

• Evaluation

• Conclusions

Questions?

• Introduction

• Demos

• Evaluation

• Conclusions

• Questions?

http://www.science.uva.nl/~msmids/afstuderen/master

MoG details

• Update Equations:)( k

tkkk E

kktkkk E )/(

22kk ))(/( 2

Z. Zivkovic, “Improved Adaptive Gaussian Mixture Model for Background Subtraction”

• MoG: ),,(*)( ,,1

, titit

itit XXP

• Introduction

• Demos

• Evaluation

• Conclusions

background subtraction for urban traffic monitoring using webcams master graduation project final...

initial background model

queue detection

urban traffic monitoring

costly slide

traffic monitoring system

extended traffic

action shadow detector

urban setting

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