Traffic Monitoring of Motorcycles during Special Events Using Video Detection

1. Traffic Monitoring of Motorcycles during Special Events Using Video Detection Dr. Neeraj K. Kanhere Dr. Stanley T. Birchfield Department of Electrical Engineering Dr. Wayne A. Sarasua, P.E. Sara Khoeini Department of Civil Engineering College of Engineering and Science Clemson University TRB 89th Annual Meeting

2. Introduction In 2006, motorcycle rider fatalities increased for the ninth consecutive year. During this period, fatalities more than doubled Significantly outpaced motorcycle registration Data from NHTSA FARS indicates disturbing trends in motorcycle safety TRB 89th Annual Meeting

3. Traffic data collection and motorcycles • In June 15, 2008 FHWA began requiring mandatory reporting of motorcycle travel as part of HPMS • Need VMT data as well as crash data to assess motorcycle safety • In September, 2008, an HPMS report indicated that the quality of MC data was questionable due to the inability and inconsistency of current traffic monitoring equipment. TRB 89th Annual Meeting

4. Challenges with motorcycles Historically, collection of motorcycle data has been a low priority. Many commercially available classification systems are generally unable to accurately capture motorcycle traffic. Emphasis in the past has been on detection. Three main reasons why motorcycles are difficult to count: light axle weight low metal mass narrow footprint TRB 89th Annual Meeting

5. Overview of this research • Significant amount of motorcycle traffic • Variety of formations • Chose a motorcycle rally Myrtle Beach, SC • Evaluate a computer vision based tracking system that can count and classify motorcycles TRB 89th Annual Meeting

6. Collecting Vehicle Class Volume Data Different types of sensors can be used to gather these data: Axle sensors Presence sensors Machine vision sensors Motorcycle classification with traditional sensors • Several manufacturers indicate their devices can detect/classify motorcycles • motorcycle classification accuracy specifications not available • we could not identify any validation studies TRB 89th Annual Meeting

7. Issueswith length based classification • Some cars are not much longer than the average motorcycle • European “city cars” are gaining popularity • Average motorcycle size is larger than ever before. • Cruisers have become very popular • Wheel base is within 10” of many subcompacts • Axle counters are especially prone to length base classification errors TRB 89th Annual Meeting

8. Loop detector Amongst the most reliable traffic Capable of collecting speed, volume, and classifications Several configurations depending on application Length based classification is most common Motorcycle detection and classification • Adjusting detector senstivity may lead • to crosstalk with trucks in nearby lanes • Classification possible w/loop arrays • Electromagnetic profiling promising TRB 89th Annual Meeting

9. Overhead and side non-intrusive devices Active and passive infrared, radar, and acoustic devices Capable of collecting speed, volume, and classifications Length based classification is most common Motorcycle detection and classification • Vehicle profiling is possible (e.g. vehicle contour) • Some specify >99% accuracy (scanning infrared) Motorcycle Travel Symposium

10. Small footprint sensors Magnetometers Capable of collecting speed, volume, and classifications Length based classification is most common Motorcycle detection and classification • Motorcycle detection and classification • is most promising with an array of • probes spaced at 3’ to 4’ intervals TRB 89th Annual Meeting

11. Axle sensors Most are intrusive (piezo). Some temporary (hose) Capable of collecting speed, volume, and classifications Several configurations depending on application Length based and weight base classification possible Motorcycle detection and classification • Weight base may be most • promising TRB 89th Annual Meeting

12. Machine Vision Sensors Proven technology Capable of collecting speed, volume, and classifications Several commercially available systems Uses virtual detection Benefits of video detection • No traffic disruption for installation • and maintenance • Covers wide area with a single camera • Provides rich visual information for manual inspection TRB 89th Annual Meeting

13. Traditional Approach to Video Detection Current systems use localized virtual detectors which can be prone to errors when camera placement in not ideal. Limitations of localized video detection Errors caused by occlusions Spill-over errors Problems with length based classification Cameras must be placed very high (to > 40’) to minimize error Motorcycle Travel Symposium

14. Research on motorcycle video detection • Significant recent work on tracking but very little related to motorcycle detection • Duan et al. present on-road lane change assistant that can identify motorcycles using AI including Support Vector Machines • Detection rates over 90% • Chiu et al. uses an occlusion detection and segmentation method using visual length and width and helmet detection. • 95% recognition rate for a field study of 42 motorcycles TRB 89th Annual Meeting

15. Clemson’s tracking approach Tracking enables prediction of a vehicle’s location in consecutive frames. TRB 89th Annual Meeting

16. Clemson System demo TRB 89th Annual Meeting

17. Algorithm Overview TRB 89th Annual Meeting

18. Simple Calibration TRB 89th Annual Meeting

19. Classification TRB 89th Annual Meeting

20. Classified vehicles TRB 89th Annual Meeting

21. Oops… TRB 89th Annual Meeting

22. Field evaluation of Clemson system • First attempt at automated motorcycle data collection at a bike rally • Literature indicated several manual efforts • Jamar type counters • Post processing video • Sturgis has been used automated counters since 1990 but only to collect total vehicle volumes TRB 89th Annual Meeting

23. Camera details • Pan-Tilt-Zoom • Autofocus with automatic exposure • 640 x 480 resolution • 30 frames per second TRB 89th Annual Meeting

24. Data collected at 2 locations TRB 89th Annual Meeting

25. Summary of Results Garden City Site Myrtle Beach Site TRB 89th Annual Meeting

26. Garden city results (both directions)

27. Garden city results (both directions)

28. Garden City results - regression analysis

29. Myrtle Beach results

30. Myrtle Beach site video TRB 89th Annual Meeting

31. Garden City site video TRB 89th Annual Meeting

32. Two directions at once (speed calibrated) TRB 89th Annual Meeting

33. Verifying speeds TRB 89th Annual Meeting

34. Conclusion Very high volumes of motorcycles Tight formations (staggered and pairs) Algorithm works in real time Future work • Improve robustness to eliminate systematic errors • Evaluate night time/low light conditions • Augment algorithim with pattern-based descriptors Motorcycle classification within 6% of actual even in extreme conditions: TRB 89th Annual Meeting

35. Thank you ! TRB 89th Annual Meeting

36. For more info please contact: Dr. Stanley T. Birchfield Dr. Neeraj K. Kanhere Department of Electrical Engineering stb@clemson.edu nkanher@clemson.edu Dr. Wayne A. Sarasua, P.E. Department of Civil Engineering sarasua@clemson.edu TRB 89th Annual Meeting