1. THE Reference in Traffic Video Detection Benjamin Schiereck, Sales Manger Traficon Germany Improving road and tunnel safety via incident management: implementing a video image processing system Video Detection Solutions

2. THE Reference in Traffic Video Detection 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 Presentation

3. THE Reference in Traffic Video Detection More Traffic More Accidents & More Cars Involved Time More Secondary Accidents & Long Traffic Jams SOLUTION? INCIDENT MANAGEMENT Introduction

4. THE Reference in Traffic Video Detection 1.Traffic Monitoring, Prevention 2.Incident Detection 3.Incident Verification 4.Driver Information 5.Incident Clearing Basic Functions of Incident Management

5. THE Reference in Traffic Video Detection 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

6. THE Reference in Traffic Video Detection Traffic Monitoring on Highway using cameras!!

7. THE Reference in Traffic Video Detection Traffic 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

8. THE Reference in Traffic Video Detection Detection Rate Incident Detection with respect to dedicated camera positions for incident detection Indoors (tunnel) OutdoorsTime to detect stopped vehicles (%)  98  95 10 sec queue (%)  99,9  99,5 2 sec inverse direction (%)  95 < 1 sec flow speed (maximal error)   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 Counti ng SpeedQueue > 98 %> 95% with errors < 5%99%

9. THE Reference in Traffic Video Detection 1.Importance to avoid traffic jams 2.Importance to avoid secondary accidents Verona, ITALYFoix, FRANCE Direct Incident detection, Time to Detect, Time to Verify Direct Incident detection, Time to Detect, Time to Verify

10. THE Reference in Traffic Video Detection Verona, ITALY Incident Management Video Based Incident Detection Incident Management Video Based Incident Detection

11. THE Reference in Traffic Video Detection Stopped vehicle Slow moving Counting Inverse direction Distances between cars Fallen objects Pedestrians Smoke Video Image Processing Functions

12. THE Reference in Traffic Video Detection System Architecture CAMERAVIPT M S

13. THE Reference in Traffic Video Detection Case 1: Fire in a Tunnel – Oslo 1996

14. THE Reference in Traffic Video Detection Data from Escota France,1999 Type of Vehicle Stopped Vehicle Visible Smoke First Visible Flames Global Fire Car0 min. 3 min.5 min.8 min. Van0 min. 5 min.8 min. 15 min. Engine fire (2%) 0 min. Fast Brake Fire (98%) 0 min. 10 min. 12 min.20 min. Evolution of Fires of Vehicles in and around Tunnels Video Image Processing Methodology

15. THE Reference in Traffic Video Detection

17. 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 - Situation

18. THE Reference in Traffic Video Detection Detection Rate False Detections False Detection Cost Reliability Öresund ORESUND : Other considerations

19. THE Reference in Traffic Video Detection 100m C1C2C3C4C5C6 100m Figure1: Distance between two cameras set at 100 metres without overlapping field of view Figure 2: Distance between two cameras set at 60 metres with overlapping field of view C1C2C3C4C5C6 60m 120m 60m Redundancy

20. THE Reference in Traffic Video Detection Video Detection examples: Tunnel Applications Smoke Detection Object Detection Pedestrian Detection Inverse Direction Detection Incident Detection Stopped Vehicle Detection

21. THE Reference in Traffic Video Detection Video Detection examples: Highway & Bridge Applications Inverse Direction Detection at night Stopped Vehicle Detection

22. THE Reference in Traffic Video 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 Summary

23. THE Reference in Traffic Video Detection 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 Conclusions

24. THE Reference in Traffic Video Detection 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

25. THE Reference in Traffic Video Detection Tel Germany +49 (0) 5446 – 20 65 32 E-mail: info@traficon.de www.traficon.com