Automatic Detection of Accidents under cctv monitoring using Machine Learning Algorithm

Abstract

In this paper, Object Detection and Tracking System (ODTS) in combination with a
well-known deep learning network, Faster Regional Convolution Neural Network (Faster R-CNN),
for Object Detection and Conventional Object Tracking algorithm will be introduced and applied
for automatic detection and monitoring of unexpected events on CCTVs in tunnels, which are
likely to (1) Wrong-Way Driving (WWD), (2) Stop, (3) Person out of vehicle in tunnel (4) Fire.
ODTS accepts a video frame in time as an input to obtain Bounding Box (BBox) results by Object
Detection and compares the BBoxs of the current and previous video frames to assign a unique ID
number to each moving and detected object. This system makes it possible to track a moving object
in time, which is not usual to be achieved in conventional object detection frameworks. A deep
learning model in ODTS was trained with a dataset of event images in tunnels to Average
Precision (AP) values of 0.8479, 0.7161 and 0.9085 for target objects: Car, Person, and Fire,
respectively. Then, based on trained deep learning model, the ODTS based Tunnel CCTV
Accident Detection System was tested using four accident videos which including each accident.
As a result, the system can detect all accidents within 10 seconds. The more important point is that
the detection capacity of ODTS could be enhanced automatically without any changes in the
program codes as the training dataset becomes rich