A New Approach To Robust Non-Intrusive Traffic Detection at Intersections!
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1 A New Approach To Robust Non-Intrusive Traffic Detection at Intersections! Chad Stelzig Algorithm Engineering, ISS! May 22 nd, 2013!
2 Todayʼs Discussion Points! Background and terminology! Single sensor detection systems! Pairing of disparate sensor types (fusion vs. independent operation)! Case study Autoscope Duo! Theoretical performance estimates and effective prototyping! Realized performance! Example video! 2!
3 Background and Terminology!! Non-Intrusive Detection Systems! Above ground detection! Elimination of road surface disruption (saw cuts)! Elimination of lane closure during installation! Robust very subjective! System level quality and capabilities! System Performance! Accurate object detection in all traffic conditions! Accurate object detection in all weather conditions! Immune to diurnal lighting changes! Ease of Use/Flexibility! Simple setup! Adaptable to evolving detection objectives! Overarching Goal! Approach loop-like detection accuracy with a nonintrusive system! 3!
4 Single Sensor Detection Systems! Any individual sensor system will exhibit strengths and weaknesses! Problematic Conditions! Shadows! Video! Radar! Nighttime Detection! High Volume Traffic! Stationary Vehicles! Wet Road! Down lane occlusion! Cross lane occlusion! Average Daytime! Average Nighttime! Observations based on standard definition CCD imager and forward fired Doppler radar! 4! 6/11/13! 2013 Image Sensing Systems!
5 Integration of Multiple Sensor Types! How do we integrate two sensors with unique Fields of View (FOV)?! Currently two schools of thought within ITS system providers! Overlapping FOV Leverage strengths of each sensor type to achieve overall improved system performance within the unified detection area! Non-Overlapping FOV Leverage characteristics of individual sensor to expand system area of coverage without improving overall system accuracy in the unique detection areas! Overlap of Sensor FOV! Non-Overlap of Sensor FOV! Integration difficulty increases when moving towards a unified fusion system! 5!
6 Example of Integration Challenge! Disparate coordinate axis homography! Location of an object in a video frame can be described using pixel coordinates! Location of an object in a radar data stream is described using real-world coordinates (x,y) relative to the sensor! Calibration procedures create a transformation (Homography) between pixel and real-world coordinate spaces! Homography matrix H! 6!
7 Autoscope Duo! Initiative in 2010 to quantify multi-sensor detection performance! Executed in stages! Independent sensor performance evaluation! Theoretical system performance estimation! Prototype, test, refinement and product rollout! The goal was to approach loop accuracy with a non-intrusive ITS solution! Video! Radar! 7!
8 Setting Performance Expectations! In any R&D activity one must be able to convey the potential upside of the new technology! Subsystem characterization is key. Evaluate the independent systems and make educated estimates of combinatorial (fusion) accuracy! Understand the environment the system will operate within! Estimated likelihood of environmental condition occurrence based on historical weather data for 11 cities across North America! 8!
9 Realized Performance! Routine monitoring of system performance allowed developers a sanity check against the expectations! Agile development of solution through early identification of issues! Expectations were known task termination once criteria were met! System evaluation across 21 hours of data! Non-shadow Daytime: 7 Hours! Rain/Wet road: 3 Hours! Nighttime: 3 Hours! Strong shadow: 5 Hours! Snow: 3 Hours! Numbers continued to improve in 2012 as system installations increased! 9!
10 Shadow Rejection! 10!
11 11!
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