Pilot Study: A Real-time Truck Parking Availability System for Kansas*

Transcription

1 Pilot Study: A Real-time Truck Parking Availability System for Kansas* Ted Morris, Vasillios Morellas, Nikolaos Papanikolopoulos Department of Computer Science and Engineering University of Minnesota *Supported by the Kansas Department of Transportation and University of Minnesota NSF Industry/University Cooperative Research Centers Program May 1-3, 2017

2 Pilot Study Objectives 1. Detect empty and occupied parking spaces using state-of-the-art computer vision techniques 2. Deliver real-time parking availability to truck drivers through roadside message boards and the TPIMS regional parking notification message sharing protocol. 3. Evaluate system wide performance through field operational testing May 1-3,

3 Synopsis Truck parking detection overview and detection approach Kansas pilot site description System architecture Preliminary results Ongoing developments May 1-3,

4 Parking Detection Automated counting of occupied parking spaces is not a straight forward problem: Entrance/Exit Trip-wire count detection (Martin, 2012, 2011; Gentler & Murray, 2011; Fallon & Howard, 2011) Small detection error bias caused occupancy count errors to accumulate at an unacceptable rate Stalls Occupied detected observed Time May 1-3,

5 Key Idea for Detection Multiple camera views reconstruct the scene in 3D. 3D reconstruction: Measures space occupancy directly by seeing the vehicles present or absent in a way similar to the way people do, in 3D. Remains robust to problems with sharp shadows, partial occlusion, and other lighting changes that traditionally confound non-3d image processing techniques No recalibration or re-zeroing. May 1-3,

6 Our Approach 1. Multi-view PTZ HD Images Acquisition D Reconstruction and alignment z y x 3. 3D background removal and occupancy classification May 1-3,

7 Previous Operational Pilot Study Tested approach by installing system at three public rest areas along Minnesota I-94, West of the Twin Cities Monitored and evaluated detection performance for over 2 years Over 95 percent accurate May 1-3,

8 Pilot Site Deployment Deployed Two 35 foot light poles with 10 foot luminaire arms and 3 PTZ HD cameras Fiber network connection to KDOT Operations Center May 1-3,

9 Pilot Site Deployment Westbound Junction City Rest Area, I-70 May 1-3,

10 Site Layout And Preparation Derived virtual parking spaces from the published capacity of 10 parking stalls Surveyed XY baseline and 56 roadway markings to use for initial alignment parameter calibration May 1-3,

11 May 1-3,

12 May 1-3,

13 System Architecture Vision Module Servers Site IP cameras Occupancy Report XML HTTP/HTTPS Nginx Web server, PostgreSQL database CMS NTCIP 1203 SNMP/TCP CMS NTCIP 1203 SNMP/TCP HTTP GET IRIS Server NTCIP MULTI (QUERY API) TPIMS Reporting and Real-time Notification Protocol (HTTP/JSON) External Services Truck Parking ** May 1-3,

14 Preliminary Results about 5 days of recorded data ground-truthed for parking space occupancy by external evaluators (SRF Consulting Group, Minnesota) Detection Accuracy Stalls 5,10 Stalls 4,9 Stalls 3,8 Stalls 2,7 Stalls 1,6 Weighted Average Occupancy 99.35% 93.72% 96.37% 98.75% 92.37% 96.21% Vacancy 85.85% 87.30% 78.90% 78.83% 92.66% 84.60% Overall 93.04% 90.25% 87.12% 87.17% 92.53% 90.01% Sample Size May 1-3,

15 Preliminary Results Parking space occupancy web server dashboard May 1-3,

16 Preliminary Results Manually counted trucks from camera observations and compared occupancy detection count reported from web server (performed by SRF Consulting Group, Minnesota) Randomly performed 28 such observations during the data collection period May 1-3,

17 Preliminary Conclusions Preliminary results indicated 90% overall detection accuracy Actual number of observed trucks parked and occupancy detection have reasonable agreement; spatial occupancy detection tended to have an over-count bias Parking behaviors observed not always conformant with intended parking stalls May 1-3,

18 Future and ongoing work Observed Undisciplined parking behavior created scenarios where truck counts nor pre-defined spatial detection occupancy counts match actual space availability. Investigating the utilization of unsupervised clustering with fast ground plane segmentation 1 defines 3D vehicle bounding boxes that will allow the system to measure spatial distances between parked vehicles Advantage of using same detection architecture Harvested more datasets for further evaluation 1 Zermas et. al, Fast Segmentation of 3D Point Clouds: A Paradigm on LiDAR Data for Automomous Vehicle Applications, accepted ICRA 2017 May 1-3,

19 Spatial 3D truck blobs Daytime clustering of the multiple aligned 3D reconstructions from multiple views May 1-3,

20 Spatial 3D truck blobs Nightime clustering of the multiple aligned 3D reconstructions from multiple views May 1-3,

21 Thanks for Listening! rosehub.umn.edu *Supported by the Kansas Department of Transportation and University of Minnesota NSF Industry/University Cooperative Research Centers Program May 1-3,