PADESTRIANSAFENET:AI-DRIVEN REALTIME PEDESTRIANDETECTIONTOPREVENTTRAFFIC ACCIDENTS

Abstract

Pedestrian safety has become a major concern in
modern transportation systems due to the
increasing number of road accidents caused by
delayed driver response, poor visibility, and lack of
intelligent monitoring systems. The proposed
system, “Pedestrian SafeNet: AI-Driven Real-Time
Pedestrian Detection to Prevent Traffic Accidents,”
presents an advanced computer vision–based
solution for detecting pedestrians and obstacles in
real time using artificial intelligence and deep
learning techniques. The system utilizes OpenCV,
YOLO-based object detection algorithms, and
machine learning models to analyze live video
streams captured through vehicle-mounted cameras
or surveillance systems. The captured video is
divided into frames, preprocessed, and analyzed to
identify pedestrians accurately under varying
environmental conditions such as low lighting,
crowded roads, and moving backgrounds. The
proposed framework also estimates the distance
between the vehicle and the detected pedestrian and
generates immediate visual and audio alerts
whenever a potential collision risk is identified.
The system improves road safety by minimizing
driver distraction and reducing dependence on
manual monitoring. Real-time processing ensures
fast response and efficient pedestrian recognition
with improved detection accuracy. The framework
can be integrated with intelligent transportation
systems, autonomous vehicles, smart city
surveillance networks, and advanced driver
assistance systems to provide enhanced public
safety. Experimental analysis demonstrates that the
proposed model achieves reliable performance in
pedestrian detection and alert generation while
maintaining computational efficiency. The research
contributes toward the development of scalable,
intelligent, and adaptive accident prevention
systems capable of improving transportation safety
and reducing pedestrian-related fatalities in realworld
scenarios.