Accurate Speed and Density Measurement for Road Traffic in India

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Accurate Speed and Density Measurement for Road Traffic in India

  1. 1. Accurate Speed and DensityMeasurement for Road Traffic in India Rijurekha Sen (IIT Bombay) Andrew Cross, Aditya Vashishtha,Venkat Padmanabhan, Ed Cutrell, Bill Thies
  2. 2. Home Offic eUser: How would travel time shifts change commute time?
  3. 3. Bengaluru Traffic Control CenterOperator: can measure traffic density, speed, and flux, and trigger automated alerts?
  4. 4. Researcher: How are different traffic parameters like speed, density and flux related?
  5. 5. Arent these solved problems?
  6. 6. Yes, but for traffic like ....Loop detectors Traffic cameras
  7. 7. While Indian traffic looks like ....
  8. 8. Prior Work to Sense Unlaned TrafficLakshminarayanan et al. DEV 2011(-) binary classification of density based on grayscale histogramswith limited evaluationQuinn et al. AAAI-D 2010(-) only detects motion of vehicles with limited evaluationTrazer from Kritikal Solutions (IIT Delhi)(-) proprietary solution costing INR 3-5 Lakhs per license(-) frontal view of traffic to match vehicle Haar features, no evaluation for density measurements in case of occlusionSen et al. Mobisys 2010, SenSys 2012 (IIT Bombay)(-) binary or 4-level classification of density(-) low accuracy for acoustic sensors, no speed for radio sensors
  9. 9. Measuring Density and Speed using Video
  10. 10. Experimental SetupStandard mounting ― Aimed at intersection
  11. 11. Experimental Setup Video recorded using Canon FS100 camcorder. Processed on IBM R61 Thinkpad laptop usingStandard mounting ― Aimed at intersection OpenCV. Indiranagar Malleshwaram Mekhri Windsor Our mounting ― Looking down on traffic
  12. 12. Density With Background Subtraction? subtracta vehicle frame an empty frame
  13. 13. But, Bengaluru buses surprised us! The tops of the buses look exactly like the road, so background subtraction yields zero density.
  14. 14. Density With Yellow Tape Analysis?Tape on road Density for empty road
  15. 15. Density With Yellow Tape Analysis?Tape on road Density for two buses
  16. 16. But, shadows surprised us!Treated as part of vehicle! Need perspective correction
  17. 17. Final Density Estimation AlgorithmSpatial condition:Does contrastbetween yellow andblack rectanglesdisappear due touniform vehicle top?
  18. 18. Final Density Estimation AlgorithmSpatial condition: Temporal condition:Does contrast Does average RGB of rectangle pixelsbetween yellow and change by more than a thresholdblack rectangles between two consecutive frames?disappear due to (Consecutive frames reduce lightuniform vehicle top? change issues.)
  19. 19. Final Density Estimation AlgorithmSpatial condition: Temporal condition:Does contrast Does average RGB of rectangle pixelsbetween yellow and change by more than a thresholdblack rectangles between two consecutive frames?disappear due to (Consecutive frames reduce lightuniform vehicle top? change issues.) Linear regression on a training vehicle set to reduce systemic under-estimation. Moving averages to extend 1-d density estimation to 2-d density estimation.
  20. 20. Speed Estimation Algorithm For pixels that moved by more than a threshold,
  21. 21. Speed Estimation Algorithm For pixels that moved by more than a threshold, search in the neighborhood of size covering high speeds, for pixels of similar RGB.
  22. 22. Speed Estimation Algorithm For pixels that moved by more than a threshold, search in the neighborhood of size covering high speeds, for pixels of similar RGB.The displacement that maximizes the similarity over all pixels, is considered speed in pixels between consecutive frames.
  23. 23. Density Algorithm Evaluation
  24. 24. Density Algorithm Evaluation
  25. 25. Density Algorithm Evaluation The relative errors are higher for smaller vehicles like two-wheelers.
  26. 26. 2-D Density Evaluation In our applications, we use moving averages over 30 seconds for density.
  27. 27. Speed Algorithm Evaluation
  28. 28. Speed Algorithm Evaluation
  29. 29. Speed Algorithm EvaluationVehicle height differences variation in speed estimates.Taller vehicle  higher speed
  30. 30. Decrease in Speed Error with Increase in Averaging Window SizeIn our applications, we use moving averages over 30 seconds for speed values.
  31. 31. Some Applications of the Density and Speed Estimates
  32. 32. Avoiding CongestionUsers would like shorter commute timesIn Indian cities, spatial shifting (rerouting) is often not effective since all routes are likely congestedAn alternative is temporal shifting of traffic (e.g., the work of Balaji Prabhakar @ Stanford)
  33. 33. Temporal Shifting 20 minutes moving averages of speed and density valuesbetween 8:15 am – 11:15 am on Jul 10, 2012 at Malleshwaram.
  34. 34. Temporal Shifting 20 minutes moving averages of speed and density valuesbetween 8:15 am – 11:15 am on Jul 10, 2012 at Malleshwaram. Speed and density are inversely related there exist opportunities for users to shift and gain. But how about the traffic authorities?
  35. 35. Estimating Fundamental Curves of Transportation Engineering High flux needs density < 40%speed vs. density flux vs. speed High flux needs speeds in 26-38 kmph range
  36. 36. Fundamental Curves of Transportation Engineering High flux values need < 40% density values. speed vs. density flux vs. speed
  37. 37. Fundamental Curves of Transportation Engineering High flux values need < 40% density values. 95% of the flux in congestion correspond to densities less than 80%, thus very high densities are outliers. Just 20% reduction in density can double the speed.flux percentages at high densities
  38. 38. Effect of Uniform Flux Redistribution Flux percentages for different speed bins for Flux percentages for different speed bins8:15 to 11:15 am, Jul 10, 2012 at Malleshwaram for flux values 4.5 – 5.5 Uniform redistribution over 3 hours  flux of 5.04. This will increase speeds for vehicles, corresponding to about 80% flux, to above 35 Km/hr.
  39. 39. ConclusionSimple, accurate density and speed estimation for un-lanedtraffic using videos.Non-trivial insights informed our algorithm design.Some applications of the density and speed estimates.Several avenues for improvement.
  40. 40. Future WorkAuto-calibration of cameras.Combination with night vision.Evaluation on temporally and spatially larger datasets.System development to reduce computation andcommunication overhead.Sharing methods and insights with the traffic authorities.

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