Authors :
Rohith G; Twinkle Roy; Vishnu Narayan V; Shery Shaju; Ann Rija Paul
Volume/Issue :
Volume 5 - 2020, Issue 6 - June
Google Scholar :
http://bitly.ws/9nMw
Scribd :
https://bit.ly/2D5aJGI
DOI :
10.38124/IJISRT20JUN432
Abstract :
This paper depicts the efficient use of CCTV for
traffic monitoring and accident detection. The system which
is designed has the capability to classify the accident and
can give alerts when necessary. Nowadays we have CCTVs
on most of the roads, but its capabilities are being
underused. There also doesn’t exist an efficient system to
detect and classify accidents in real time. So many deaths
occur because of undetected accidents. It is difficult to
detect accidents in remote places and at night. The
proposed system can identify and classify accidents as
major and minor. It can automatically alert the authorities
if it deals with a major accident. Using this system the
response time on accident can be decreased by processing
the visuals of CCTV.
In this system different image processing and machine
learning techniques are used. The dataset for training is
extracted from the visuals of already occurred accidents.
Accidents mainly occur because of careless driving, alcohol
consumption and over speeding. Another main cause of
death due to accidents are the delay in reporting
accidents since there doesn’t exist any automated systems.
Accidents are mainly reported by the public or by traffic
authorities. We can save many lives by detecting and
reporting the accident quickly. In this system live video is
captured from the CCTV’s and it is processed to detect
accidents. In this system the YOLOV3 algorithm is used for
object detection. Nowadays traffic monitoring has a greater
significance. CCTV’s can be used to detect accidents since it
is present in most of the roads. It is only used for traffic
monitoring. Normally accidents can be classified as two
classes major and minor. The proposed system is able to
classify the accident as major or minor by object detection
and tracking methodologies. Every accident doesn’t need
emergency support. Only major accidents must be handled
quickly. The proposed system captures the video and
undergo object detection algorithms to identify the different
objects like vehicles and people. After the detection phase
Keywords :
YOLO V3, SSD , Faster RCNN , RCNN.
This paper depicts the efficient use of CCTV for
traffic monitoring and accident detection. The system which
is designed has the capability to classify the accident and
can give alerts when necessary. Nowadays we have CCTVs
on most of the roads, but its capabilities are being
underused. There also doesn’t exist an efficient system to
detect and classify accidents in real time. So many deaths
occur because of undetected accidents. It is difficult to
detect accidents in remote places and at night. The
proposed system can identify and classify accidents as
major and minor. It can automatically alert the authorities
if it deals with a major accident. Using this system the
response time on accident can be decreased by processing
the visuals of CCTV.
In this system different image processing and machine
learning techniques are used. The dataset for training is
extracted from the visuals of already occurred accidents.
Accidents mainly occur because of careless driving, alcohol
consumption and over speeding. Another main cause of
death due to accidents are the delay in reporting
accidents since there doesn’t exist any automated systems.
Accidents are mainly reported by the public or by traffic
authorities. We can save many lives by detecting and
reporting the accident quickly. In this system live video is
captured from the CCTV’s and it is processed to detect
accidents. In this system the YOLOV3 algorithm is used for
object detection. Nowadays traffic monitoring has a greater
significance. CCTV’s can be used to detect accidents since it
is present in most of the roads. It is only used for traffic
monitoring. Normally accidents can be classified as two
classes major and minor. The proposed system is able to
classify the accident as major or minor by object detection
and tracking methodologies. Every accident doesn’t need
emergency support. Only major accidents must be handled
quickly. The proposed system captures the video and
undergo object detection algorithms to identify the different
objects like vehicles and people. After the detection phase
Keywords :
YOLO V3, SSD , Faster RCNN , RCNN.
