Benjamin Schiereck, Sales Manger Traficon Germany

THE Reference in Traffic Video DetectionTHE Reference in Traffic Video Detection

Benjamin Schiereck, Sales Manger Traficon Germany

Improving road and tunnel Improving road and tunnel safety via incident safety via incident management:management: implementing a video image implementing a video image processing systemprocessing system

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• Introduction• Incident Management: Video Based Incident

Detection Basic Functions of Incident Management Video Image Processing Functions, Methodology & System

architecture Detection rate Automatic Incident Detection system Cases (Eye on fire in a tunnel) Typical Freeway and Tunnel AVI files

• Summary• Conclusions

Outline of PresentationOutline of Presentation


More Traffic

More Accidents

&

More Cars

Involve

d

Time

More Secondary

Accidents

&

Long Traffic Ja

ms

SOLUTION?

INCID

ENT

INCID

ENT

MANAGEMENT

MANAGEMENT

IntroductionIntroduction


1. Traffic Monitoring, Prevention

2. Incident Detection

3. Incident Verification

4. Driver Information

5. Incident Clearing

Basic Functions of Incident ManagementBasic Functions of Incident Management


Traffic Monitoring – Prevention Traffic Monitoring – Prevention

• Most important is safe infrastructure

• Monitor traffic situation, speed and occupancy using video cameras.

• Set appropriate speeds on VMS panels

• Fast information about the incident

• Fast reaktion on incident– e.G.. Closing the Tunnel

All about time


Traffic Monitoring on HighwayTraffic Monitoring on Highway

using cameras!!


Traffic Monitoring in TunnelTraffic Monitoring in Tunnel

• Access control, situation in the Tunnel• Monitoring actions with video

1. Slow driving vehicle

2. Traffic jam in tunnel

3. Speed differences

4. Occupancy

5. Intervehicle distances

• VMS Panels• Ventilation control


Detection RateDetection RateIncident Detection with respect to dedicated camera positions for incident detection

Indoors (tunnel)

Outdoors Time to detect

stopped vehicles (%) 98 95 10 sec

queue (%) 99,9 99,5 2 sec

inverse direction (%) 95 95 < 1 sec

flow speed (maximal error) 10 % 10 %

false alarm frequency(per camera / per day)

0,025 0,15

Data collection for Outdoors Applications and for dedicated camera positions for data collection

Counting

Speed Queue

> 98 % > 95% with errors < 5% 99%


1. Importance to avoid traffic jams

2. Importance to avoid secondary accidentsVerona, ITALY Foix, FRANCE

Direct Incident detection,Time to Detect, Time to VerifyDirect Incident detection,Time to Detect, Time to Verify


Verona, ITALY

Incident ManagementVideo Based Incident DetectionIncident ManagementVideo Based Incident Detection


• Stopped vehicle• Slow moving• Counting• Inverse direction• Distances between cars• Fallen objects• Pedestrians• Smoke

Video Image Processing FunctionsVideo Image Processing Functions


System Architecture System Architecture

CAMERA VIP T M S


Case 1: Fire in a Tunnel – Oslo 1996Case 1: Fire in a Tunnel – Oslo 1996


Data from Escota France,1999

Type of Vehicle

Stopped Vehicle

Visible Smoke

First VisibleFlames

Global Fire

Car 0 min. 3 min. 5 min. 8 min.

Van 0 min. 5 min. 8 min. 15 min.

Engine fire (2%)

0 min. Fast Fast Fast

Brake Fire (98%)

0 min. 10 min. 12 min. 20 min.

Evolution of Fires of Vehicles in and around Tunnels

Video Image Processing MethodologyVideo Image Processing Methodology




• Because this is quite a long tunnel at under sea level, the owner requested a highly redundant system with a very high detection rate, high reliability (MTBF) and a very high level of service (% Uptime).

• This was one of the reasons why the detection cameras were installed at 60 metres distance, but programmed to cover at least 120 m.

Case 2: ORESUND - SituationCase 2: ORESUND - Situation


• Detection Rate• False Detections• False Detection Cost• Reliability

Öresund

ORESUND : Other considerationsORESUND : Other considerations


100m 100m 100m 100m 100mC1 C2 C3 C4 C5 C6

100m

FigureFigure11: Distance between two cameras set at 100 metres without : Distance between two cameras set at 100 metres without overlapping field of viewoverlapping field of view

FigureFigure 2 2: Distance between two cameras set at 60 metres with : Distance between two cameras set at 60 metres with overlapping field of view overlapping field of view

C1 C2 C3 C4 C5 C660m 60m 60m 60m 60m

120m

60m 60m

RedundancyRedundancy


Video Detection examples: Tunnel ApplicationsVideo Detection examples: Tunnel Applications

Smoke Detection

Object Detection

Pedestrian Detection

Inverse Direction Detection

Incident Detection

Stopped Vehicle Detection


Video Detection examples: Highway & Bridge ApplicationsVideo Detection examples: Highway & Bridge Applications

Inverse Direction Detection at night

Stopped Vehicle Detection


Basic Advantages of Video Based Incident Detection:

• Fast incident detection rate• Visual verification • High system reliability• Easy to install and modify• Low false detection rate & cost• Low overall lifetime cost

SummarySummary


• Video detection works reliable.

• Video detection is the fastest way to detect.

• Video detection has the lowest false alarms rate.

• Video detection offers immediate verification via CCTV.

• AID, Automatic Incident detection is the best detection method for Incident management

ConclusionsConclusions


Why use other incident detection if you will verify by video?

Why use other incident detection if you will verify by video?

Just use Video Incident detection

Directly

Thank You

Just use Video Incident detection

Directly

Thank You


Tel Germany +49 (0) 5446 – 20 65 32E-mail: [email protected]

www.traficon.com

Documents

Benjamin Schiereck, Sales Manger Traficon Germany