Transcript Real-Time Detection and Tracking of Vehicles for Measuring

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
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
TRB 89th Annual Meeting
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
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
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Overview of this research
Evaluate a computer vision based tracking system
that can count and classify motorcycles
Significant amount of motorcycle traffic
Variety of formations
Chose a motorcycle rally Myrtle Beach, SC
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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
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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 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
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
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
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
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
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
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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
Motorcycle
Travel
Symposium
Cameras must be placed
very
high
(to > 40’) to minimize error
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
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Clemson’s tracking approach
Tracking enables prediction of a vehicle’s location in
consecutive frames.
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Clemson System demo
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Algorithm Overview
Background
subtraction
Foreground mask
Input frame
KLT tracking
Stable feature groups
Classification
Calibrationparameters
Tracked vehicles
Matching
Initialize new vehicles
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Simple Calibration
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Classification
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Classified vehicles
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Oops…
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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
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Camera details
Pan-Tilt-Zoom
Autofocus with
automatic exposure
640 x 480 resolution
30 frames per second
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Data collected at 2 locations
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Summary of Results
Garden City Site
Approaching
Departing
Total
MC 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
Myrtle Beach Site
MC
PC and HV
Total
Actual
Counts
726
333
1059
System
Result
681
321
1002
Dif
(Percents)
-6.19
-3.60
-5.38
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Garden city results (both directions)
1600
Number of MCs
1400
1200
1000
800
MC Cum Manual
Counts
600
MC Cum Sys Counts
400
200
0
5
10 15 20 25 30 35 40 45
Time (min)
Garden city results (both directions)
8
6
Difference (%)
4
2
0
MC
-2
PC & HV
-4
Total
-6
-8
0
2
4
6
5 Minute Interval
8
10
Garden City results - regression analysis
Cum Actual Counts vs. System Counts
3000
2500
Actual Counts
2000
1500
PC & HV
MC
All Vehicles
1000
500
Slope
PC & HV
1.0009
MC
0.9861
All Vehicles
0.9925
R-Sq
1.0000
0.9998
1.0000
0
0
500
1000
1500
System Counts
2000
2500
3000
Myrtle Beach results
MC 5 min Counts vs. Time
120
100
Number of MCs
80
60
MC Manual- 5min
MC System - 5 min
40
20
0
5
10
15
20
25
Time (min)
30
35
40
45
Myrtle Beach site video
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Garden City site video
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Two directions at once (speed calibrated)
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Verifying speeds
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Conclusion
Motorcycle classification within 6% of actual even
in extreme conditions:
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
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Thank you !
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For more info please contact:
Dr. Stanley T. Birchfield
Dr. Neeraj K. Kanhere
Department of Electrical Engineering
[email protected]
[email protected]
Dr. Wayne A. Sarasua, P.E.
Department of Civil Engineering
[email protected]
TRB 89th Annual Meeting