1. Airport Parking Space Navigation
Pingbo Pan, Ming Kong, Ting-Yao Hu, and Alex Hauptmann
Language Technologies Institute, School of Computer Science, Carnegie Mellon University
ABSTRACT
Motivation: finding empty, convenient parking lot for user.
Vacant Parking Space Navigation (VPSN) system detects vacant parking slots through camera and provide guidance to customers.
VPSN system contains the following four modules:
Parking Space Surveillance
Vacant Space Detection
Real-time Car Tracking
Mobile Navigation App
We setup cameras in Pittsburgh Airport parking area to acheive surveillance, collect data, and develop algorithms for for detection and tracking tasks.
CAR TRACKING
Locating the moving car in monitor screen in real time.
Challenges:
severe lighting/weather conditions
object (car) angle changes a lot.
real time process
many distractors (cars in the parking slots)
Approach: Tracking with classification
given tracking result of frame, propose bounding box candidates.
validate these candidates via classifiers
Our algorithm performs well in accuracy, but not in efficiency.
Next steps: consdering multiple cameras.
PARKING SPACE SURVEILLANCE
Incorporate with Pittsburgh Airport parking office.
5 ip cameras with different views setup around a section of long term parking area in Pittsburgh Airport
A large scale video dataset for parking space surveillane collected
~8.8k hours (1.5 TB)
Data in different weather, lighting conditions
Potentially suitable for other event detection tasks
VPSN System
Vacant Space Detection
Car Tracking
Mobile Navigation App
current location
destination
Parking Space Surveillance
VACANT SPACE DETECTION
VPSN system detect the available parking slots from surveillance image.
Calibration: given the intrinsic/extrinsic parameters of camera, find out the parking slot regions.
~14k images annotated for experiment
manual mapping used instead of automatic calibration
formulate a binary classification problem
Convolutional Neural Network (CNN) applied as classifier.
In a preliminary experiment, we can achieve 0.95/0.94 precision/recall. .
MOBILE NAVIGATION APP
Turn-by-turn navigation service on mobile device
Communicating with VSD and CT modules
VSD module helps identifying the destination
CT module report the current location of user’s car
GPS signal enhance the instant report of current location
adopting GPS signal only? Accuracy 3.5m at best (usually worse)
hybrid approach
Real-time navigation
Android/iOS version
Calibration &
Segmentation
Input Image
Detection Result
CNN classifier
CONCLUSION AND FUTURE WORK
Current progress shows the feasibility of VPSN system.
Future works:
improve the performance and efficiency of each modules
full integration of all the functionality
collecting data from real users
SlidesMap
Parking lot tool
Why tracking with classification
Turn left, and the empty slot will be on your right.