Car Parking Space Finder Using Deep Learning.

CAR PARKING SPACE FINDER USING DEEP LEARNING
Abstract
In big cities people faces many problems related to occupancies. One of the major
problem faced nowadays is parking problem. With increase in population vehicles are also
increased day by day which leads to shortage of car parking spaces. Thus, to find parking space
in big cities are sometime frustrating. Thus to overcome this situation it is necessary to build
something which can tell or predict if there is any vacant space for parking in the nearby
location. Also due to car parking there could be heavy traffic jam which can lead to severe
accidents. Thus keeping in mind a system need to be build which will be able to break down the
problem.
Research Area Overview
Using recent widely used technology mainly machine learning, deep learning and
artificial intelligence situation can be solved and can be managed with proper instructions. It is
really very much complicate to build such technology using machine learning and deep learning
for this first it is necessary to break down the problem into a sequence for simple tasks. Then
according to the breakdown and it will be easy to pull different tools from the machine learning
toolbox to solve each of the smaller tasks. By chaining together several small solutions into a
pipeline, we’ll have a system that can do something complicated (Acharya, Yan and
Khoshelham, 2018). The data will be a video which will be provided as an input to the machine
learning model which is generally a stream from a webcam pointed out of the window.
Related Works
Through pipeline each frame need to be passed and it should be one at a time. The initial
stage involves the to capture and detects all the possible parking spaces available in the frame of
the video, thus for these it is necessary to know which portion of the image contains the parking
slot so that it will be easy for the program to identify which parking spaces are occupied and
which are not. Then comes the second which is same procedure as the above here it is necessary
to detect the cars in each frame of the video which will eventually help to track the movement
and space occupied by each car from frame to frame (Amato, et al., 2017). The third step is to

determine which of the parking spaces are currently occupied by cars and which aren’t. This
requires combining the results of the first and second steps. Thus the third step is crucial for any
model or pipeline as if there are any vacant space observed in the frame then only it can be said
to any driver that in that particular zone there is space available for parking. And after which the
last step includes to send notification when a parking space becomes newly available and it will
be only possible if there is any car position change between the frames of the video (Kemker,
Salvaggio and Kanan, 2018).
The projected task is bit hard to implement but possible to accomplish each of these steps
a number of different ways using a variety of technologies (Amato et.al, 2016). There’s no single
right or wrong way to build this pipeline and different approaches will have different advantages
and disadvantages. Thus according to the pipeline neural network model will be implemented
where the data in the form of video will be fetched to classify the vacant parking space and the
cars.
Research Problems and Questions
Few problems which can hamper the performance in transfer learning including the
shadows of the buildings on the parking spaces, strong solar reflection from the vehicles,
vehicles parked outside or in between the designated bays by the drivers and the bias of the
training data used. Also during night time it will be challenging for any model to correctly
classify most of the things which raise a question whether it will be useful during the night time
or not. According to the thinking of the buildup the process will be cost effective or not if not
then due to what the cost increases. According to the system huge storing capacity needed to
store the video which is a challenging task and constant monitoring needed which is also
difficult.
Research Methodology
At first parking spaces need to be detected using the camera thus it is important to scan the
image and get back a list of areas that are valid to park in. The vacant spaces can be identified if
there is any non-moving can then it will be easy to spot the places easily (Camero et al., 2018).
Thus if somehow possible to detect cars and figure out which ones aren’t moving between
frames of video, then it can be taken to consideration that those spaces are parking lots.