Real-Time Event-Driven Road Traffic Monitoring System Using CCTV Video Analytics

Abstract

Closed-circuit television (CCTV) systems have become pivotal tools in modern urban surveillance and traffic management, contributing significantly to road safety and security. This paper introduces an effective solution that capitalizes on CCTV video analytics and an event-driven framework to provide real-time updates on road traffic events, enhancing road safety. Furthermore, this system minimizes the storage requirements for visual data while retaining crucial details related to road traffic events. To achieve this, a two-step approach is employed: (1) training a Deep Convolutional Neural Network (DCNN) model using synthetic data for the classification of road traffic (accident) events and (2) generating video summaries for the classified events. Privacy laws make it challenging to obtain extensive real-world traffic data from open-source datasets, and this challenge is addressed by creating a customised synthetic visual dataset for training. The evaluation of the synthetically trained DCNN model is conducted on ten real-time videos under varying environmental conditions, yielding an average accuracy of 82.3% for accident classification (ranging from 56.7% to 100%). The test video related to the night scene had the lowest accuracy at 56.7% because there was a lack of synthetic data for night scenes. Furthermore, five experimental videos were summarized through the proposed system, resulting in a notable 23.1% reduction in the duration of the original full-length videos. Overall, this proposed system holds significant promise for event-based training of intelligent vehicles in Intelligent Transport Systems (ITS), facilitating rapid responses to road traffic incidents and the development of advanced context-aware systems.

Keywords

• DCNN
• event classification
• intelligent transport systems
• synthetic data
• video summarization