Xerox Vehicle Passenger Detection System - ITS …...• Xerox Vehicle Passenger Detection System...
Transcript of Xerox Vehicle Passenger Detection System - ITS …...• Xerox Vehicle Passenger Detection System...
Frederic RoullandXerox Research Centre Europe
Xerox Vehicle Passenger Detection System
Intelligent road traffic system trends
• From infrastructure to vehicle centric
• Ticket less, barrier less, free flow roads/parking lots
• On-board units, connected, autonomous vehicles
• Aim is to ease traffic
• From vehicle to traveler centric
• Mobility as a Service vs vehicle ownership
• Shared occupancy vs single occupancy
• Aim is to reduce traffic footprint
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IdentifyVehicle and types
PeopleUser Privacy
TrackVehicle point to
point vs continuous
People behavior
ExecuteOnline tracking
Fast and lightweight models
LearnDomain adaptation
Training data capture/generation
Why do we need computer vision?
• Detect, track and identify people and vehicles
• For safety
• Speed
• Incident detection
• Dangerous behaviors detection
• Demand management
• Vehicle occupancy incentive
• Dynamic Road Tolling enforcement
• Dynamic Parking enforcement
• Internal security
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• How can research help?
Xerox transport innovations using computer vision
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Open road tolling
Vehicle occupancy enforcement
Car Park management
On street parking enforcement
Public transport crowd management
Vehicle Occupancy enforcement
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CLOUD
LOCAL
PROCESSING
TICKET
POLICE OFFICER
IN BACK OFFICE
Applications
• Enforce Car pooling incentives – Dedicated (HOV) lanes
• Caltrans I5: January 2015 (ITS America Award for Best Partnership Project in Infrastructure of Things category)
– Dedicated gates• French border – June 2015
(CEREMA award for best partnership project)
– Specific toll fares
• Automate Passenger counting – Border crossing inspection
• pilot – Ongoing
– Analytics
Pilot at France’s border
3: Prediction: 0, 1, 2 or more passengers
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1: Windshield detection 2: Side window detection
System accuracy
• Correctly predicts the number of passenger in 93.5% of the cars in overall
conditions (95.94% in Caltrans)
• Detects Single Occupancy Vehicle (SOV) with an accuracy of 95%.
Occupancy rate
Date / time Number of passengers
None 1 1 or more 2 or more
Global 1.36 June 3-6 95,1 % 91,8 % 92,5 % 95,2 %
Night 1,19 June 3-4
4h30 AM- 5h15 AM97,0 % 95,4 % 95,2 % 98,2 %
High Traffic 1,17 June 3-5
4h30 AM - 8h00 AM96,8 % 96,9 % 97,1 % 98,6 %
Low Traffic 1,53 June 3-5
8h00 AM - 5h00 PM93,8 % 87,4 % 88,5 % 92,3 %
Highoccupancy
1,88 Saturday June 6 1h00
PM - 4h00 PM91,6 % 80,4 % 81,2 % 85,4 %
At the core of the technology:
XRCE’s Generic Visual Toolbox (GVT)
• Towards holistic visual understanding
• Detecting and recognizing scene elements (objects, text, etc.)
• Tracking scene elements and interactions
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Genericity id achieved by learning from the data
GVT facts
Approx. 120 patents and patent applications protecting
‒ the core technology itself: signatures, classification, …
‒ different applications and businesses: transportation, retail, BPO, …
‒ 18 international awards over the past 10 years
Developed in collaboration with some of the leaders in the field
‒ in the US: MIT, Harvard, UIUC
‒ in Europe: Oxford, EPFL, INRIA
Stable C/C++ code developed, enriched, fine-tuned over past 12 years
→ robust software components, difficult to replicate
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Conclusion
• Xerox Vehicle Passenger Detection System
– An interesting example of new generation of visual sensors going
beyond car tracking
– Successfully piloted across the world
– Relying on powerful proprietary computer vision technology
– A wide potential of applications for car pooling incentives and people
counting