1. TRB 89 th Annual Meeting 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

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

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 89 th Annual Meeting

4. Challenges with motorcycles Three main reasons why motorcycles are difficult to count: light axle weight low metal mass narrow footprint Three main reasons why motorcycles are difficult to count: light axle weight low metal mass narrow footprint 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.

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

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

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

9. Motorcycle Travel Symposium 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 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)

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

11. TRB 89 th Annual Meeting 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 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  Weight base may be most promising Motorcycle detection and classification

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

13. Motorcycle Travel Symposium Traditional Approach to Video Detection 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 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 Current systems use localized virtual detectors which can be prone to errors when camera placement in not ideal. Current systems use localized virtual detectors which can be prone to errors when camera placement in not ideal.

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 89 th Annual Meeting

15. Clemson’s tracking approach Tracking enables prediction of a vehicle’s location in consecutive frames. Tracking enables prediction of a vehicle’s location in consecutive frames.

16. Clemson System demo TRB 89 th Annual Meeting

17. Algorithm Overview TRB 89 th Annual Meeting

18. Simple Calibration TRB 89 th Annual Meeting

19. Classification

20. Classified vehicles TRB 89 th Annual Meeting

21. Oops…

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 89 th Annual Meeting

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

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

25. Summary of Results TRB 89 th Annual Meeting ApproachingDepartingTotal MC Actual Counts 8056841489 MC System Counts 7847141498 MC Percent of Difference -2.614.380.6 PC and HV Actual Counts 5805981178 PC and HV System Counts 5935821175 PC and HV Percent of Difference 2.24-2.67-0.25 Total Actual Counts 138512822667 Total System Counts 137712962673 Total Percent of Difference -0.57 1.09 0.22 Actual Counts System Result Dif (Percents) MC 726681-6.19 PC and HV 333321-3.60 Total 10591002-5.38 Myrtle Beach Site Garden City Site

26. Garden city results (both directions)

28. Garden City results - regression analysis PC & HVMCAll Vehicles Slope1.00090.98610.9925 R-Sq1.00000.99981.0000

29. Myrtle Beach results

30. Myrtle Beach site video TRB 89 th Annual Meeting

31. Garden City site video TRB 89 th Annual Meeting

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

33. Verifying speeds TRB 89 th Annual Meeting

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

35. TRB 89 th Annual Meeting Thank you !

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