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 PE ID: 649866
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TRB 89th Annual Meeting
Traffic Monitoring of Motorcycles during Special Events Using Video Detection
Dr. Neeraj K. KanhereDr. Stanley T. BirchfieldDepartment of Electrical EngineeringDr. Wayne A. Sarasua, P.E.Sara KhoeiniDepartment of Civil Engineering
College of Engineering and Science
Clemson UniversitySlide2
TRB 89th 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 doubledSignificantly outpaced motorcycle registrationSlide3
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 safetyIn 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 Slide4
TRB 89th Annual Meeting
Challenges with motorcycles
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. Slide5
Overview of this research
Significant
amount of motorcycle trafficVariety of formations Chose a motorcycle rally Myrtle Beach, SC TRB 89th Annual Meeting Evaluate a computer vision based tracking system that can count and classify motorcyclesSlide6
TRB 89th 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 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 sensorsSlide7
I
ssues
with length based classificationSome cars are not much longer than the average motorcycleEuropean “city cars” are gaining popularityAverage motorcycle size is larger than ever before.Cruisers have become very popularWheel base is within 10” of many subcompactsAxle counters are especially prone to length base classification errorsTRB 89th Annual Meeting Slide8
TRB 89th Annual Meeting
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 Adjusting detector senstivity may lead to crosstalk with trucks in nearby lanes
Motorcycle detection and classification
Classification possible w/loop arrays
Electromagnetic profiling promisingSlide9
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 commonMotorcycle detection and classification Vehicle profiling is possible (e.g. vehicle contour)
Some specify >99% accuracy (scanning infrared)Slide10
TRB 89th Annual Meeting
Small footprint sensors
Magnetometers Capable of collecting speed, volume, and classifications Length based classification is most commonMotorcycle detection and classification is most promising with an array of probes
spaced at 3’ to 4’ intervals
Motorcycle detection and classification Slide11
TRB 89th 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 Weight base may be most promising
Motorcycle detection and classification Slide12
TRB 89th Annual Meeting
Machine Vision Sensors
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 detectionSlide13
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 errorCurrent systems use localized virtual detectors which can be prone to errors when camera placement in not ideal.Slide14
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 MachinesDetection 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 motorcyclesTRB 89th Annual Meeting Slide15
TRB 89th
Annual Meeting
Clemson’s tracking approach Tracking enables prediction of a vehicle’s location in consecutive frames. Slide16
Clemson System demo
TRB 89th Annual Meeting Slide17
Algorithm Overview
TRB 89th Annual Meeting Slide18
Simple Calibration
TRB 89th Annual Meeting Slide19
Classification
TRB 89th Annual Meeting Slide20
Classified vehicles
TRB 89th Annual Meeting Slide21
Oops…
TRB 89th Annual Meeting Slide22
Field evaluation of Clemson system
First attempt at automated motorcycle data collection at a bike rally
Literature indicated several manual effortsJamar type countersPost processing videoSturgis has been used automated counters since 1990 but only to collect total vehicle volumesTRB 89th Annual Meeting Slide23
Camera details
Pan-Tilt-Zoom
Autofocus with automatic exposure640 x 480 resolution30 frames per secondTRB 89th Annual Meeting Slide24
Data collected at 2 locations
TRB 89th Annual Meeting Slide25
Summary of Results
TRB 89th Annual Meeting
ApproachingDepartingTotalMC Actual Counts
805
684
1489
MC System Counts
784
714
1498
MC Percent of Difference
-2.61
4.38
0.6
PC and HV Actual Counts
580
598
1178
PC and HV System Counts
593
582
1175
PC and HV Percent of Difference
2.24
-2.67
-0.25
Total Actual Counts
1385
1282
2667
Total System Counts
1377
1296
2673
Total Percent of Difference
-0.57
1.09
0.22
Actual Counts
System Result
Dif (Percents)
MC
726
681
-6.19
PC and HV
333
321
-3.60
Total
1059
1002
-5.38
Myrtle Beach Site
Garden City SiteSlide26
Garden city results (both directions)Slide27
Garden city results (both directions)Slide28
Garden City results - regression analysis
PC & HVMCAll Vehicles
Slope
1.0009
0.9861
0.9925
R-Sq
1.0000
0.9998
1.0000Slide29
Myrtle Beach resultsSlide30
Myrtle Beach site video
TRB 89th Annual Meeting Slide31
Garden City site video
TRB 89th Annual Meeting Slide32
Two directions at once (speed calibrated)
TRB 89th Annual Meeting Slide33
Verifying speeds
TRB 89th Annual Meeting Slide34
TRB 89th Annual Meeting
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 workSlide35
TRB 89th Annual Meeting
Thank you !Slide36
TRB 89th Annual Meeting
For more info please contact:
Dr. Stanley T. BirchfieldDr. Neeraj K. KanhereDepartment of Electrical Engineeringstb@clemson.edunkanher@clemson.eduDr. Wayne A. Sarasua, P.E.Department of Civil Engineering
sarasua@clemson.edu