CCTV BASED VECHILE CRASH DETECTION A TENSORFLOW FOR IMPROVED ROAD SAFETY
: https://doi.org/10.65725/JCISE/2/4/005
JOURNAL OF COMPUTATIONAL INTELLIGENCE SCIENCE AND ENGINEERING (JCISE) 
ISSN: 3107-8168
Volume 2 Issue 4, Oct – Dec 2026

Abstract: 
Road accidents are a major public safety concern, causing significant loss of human lives, severe injuries, property damage, and traffic congestion. Rapid detection of vehicle crashes is essential for reducing emergency response time and improving road safety. Conventional accident detection methods often rely on manual CCTV monitoring, emergency calls, or vehicle-mounted sensors, which may have limitations in terms of response time, coverage, and infrastructure requirements. To address these challenges, this paper proposes a CCTV-Based Vehicle Crash Detection System using TensorFlow and Deep Learning. The proposed system utilizes existing CCTV surveillance cameras installed on roads, highways, intersections, and other accident-prone locations to continuously monitor vehicle movements. The captured video streams are processed using computer vision and deep learning techniques to detect vehicles and analyze their motion patterns. TensorFlow is employed to develop and implement the deep learning model, which identifies abnormal vehicle behavior and potential collision events. The system considers visual parameters such as vehicle position, movement direction, speed variation, sudden braking or stopping, trajectory overlap, and changes in motion patterns to distinguish normal traffic conditions from possible crashes. When a potential accident is detected, the system automatically generates an accident alert, reducing the need for continuous human supervision. Relevant video frames or footage can be stored to assist authorities in verifying the incident and analyzing its circumstances. The proposed system can further be integrated with IoT communication systems, GPS, emergency services, traffic management centers, and notification platforms to provide accident location information and facilitate faster emergency response. The effectiveness of the proposed system can be evaluated using performance measures such as accuracy, precision, recall, F1-score, false alarm rate, and detection time. Proper model training and validation can improve crash-detection reliability while reducing false alarms caused by normal traffic activities. By utilizing existing CCTV infrastructure, the proposed approach can be deployed in smart cities, highways, urban roads, tunnels, and busy intersections with comparatively low additional infrastructure requirements. Overall, the integration of CCTV surveillance, TensorFlow, deep learning, computer vision, and real-time alert mechanisms provides a promising approach for automated vehicle crash detection. The proposed system has the potential to improve emergency response, support intelligent traffic management, reduce the impact of road accidents, and contribute to the development of safer and more efficient intelligent transportation systems.

Authors:  Dr. A. Jayasudha, Vigneshwar J.K, Sai Priyadharsan S

Keywords: Vehicle Crash Detection, Computer Vision, Real-Time Accident Detection, Video Surveillance, Emergency Notification System, Intelligent Transportation System, Smart City, Event Detection.