TY  - JOUR
AU  - T. Sai Lalith Pasad
AU  - Voruganti Lokesh
AU  - Bunga Sampath
AU  - Bathula Hariteja
PY  - 2025
DA  - 2025/06/27
TI  - Accident Detection and Alert System
JO  - Global Journal of Engineering Innovations and Interdisciplinary Research
VL  - 5
IS  - 4
AB  - Road safety is a critical concern, with traffic accidents posing significant risks to human life and
infrastructure. Accidents are considered one of the most serious problems for human population and
cause great disruption to traffic flow worldwide. The effects especially loss of life and disruption
of traffic could be addressed by timely detection of these events in real time and notifying relevant
authorities. The goal of this proposal is to research on how to design and implement Automated Accident
Detection and Notification System by using modern day image analysis and deep learning tools like
CNN. Using cameras as input, the system detects unusual vehicle dynamics such as sudden stops and
irregular vehicle movements that are precursors of an impending accident. In the case where additional
learning is required to classify events, the proposed solution incorporates the use of OpenCV to preprocess and retrieve motion information from video sequences and a set of CNN models to classify the
learned features of the encapsuled event video related to an accident.
The system presented progresses the new studies in which, through the use of video, irregularities in
traffic flows are quantified. Borrowing techniques of motion estimation like the Farneback Optical Flow
and adaptive thresholding for anomaly detection, a solution to the problems is presented in the form of
deep learning models. The objective is for accuracy though at low computation making it deployable in
different settings like highways and urban intersections. The final goal of the project is improving road
safety in general by fast-tracking responses during emergencies and limiting the aftermath of traffic
accidents.
SN  - 3066-1226
UR  - https://dx.doi.org/10.33425/3066-1226.1130
DO  - 10.33425/3066-1226.1130