An Approach to Recognize Vehicles Context Flow for Smartphone-Based Outdoor Parking Using Supervised Machine Learning Classifiers

Abstract

Finding an available parking space
in outdoor environments such as university
campuses and roadsides need a good parking
system. In a general parking system, detecting a
vehicle entering leaving the parking premise is
one of the major steps. Currently, there are
parking systems that use cameras or
external sensors to detect the leaving and
entering of the automobiles. Both parking
systems need very high cost of deployment
and maintenance. To encounter the issues,
this paper presents a parking system for
outdoor parking systems using only
smartphone-oriented sensors. The proposed
approach does not require additional sensors
installation nor manpower support. It takes
the inputs from smartphones to detect the
driver’s context that is used to recognize the flow
of the vehicle. Context flow recognition indicates
whether a driver is parking or unparking his/her
vehicle. Supervised classifiers like support vector
machines (SMV) and decision trees (DT) are
used to recognize the parking or unparking
actions to enable vehicles tracking in the parking
area. Outcome of the proposed approach is a
significant contribution for outdoor parking as it
solely utilizes the sensors smartphones embedded
to detect parking behaviors.