Abstract
Highway accidents significantly impact normal traffic flow. Consequently, automatic detection of abnormal traffic events has gradually attracted the attention of researchers interested in intelligent transportation system. This work presents a vision-based approach for automatic traffic congestion and incident detection. The proposed approach involves extracting entropy-based features to create a grid model that simulates dynamic traffic flow behavior. When an unusual event occurs in the lane of the vehicle employing the system, the system can immediately detect it and issue signals to approaching vehicles to prevent accidents. Experiments conducted using various simulation results clearly demonstrate the validity and effectiveness of the proposed approach for managing traffic congestion and detecting incidents.
Highlights
Several investigations have suggested that most highway accidents result from primary traffic incidents
Simulation results indicated that the proposed method had low false alarm and high detection rate under uniform traffic volume [7]
The system was applied at two different locations over several months and proved highly effective with few false alarms owing to illumination changes and heavy traffic
Summary
Several investigations have suggested that most highway accidents result from primary traffic incidents. Kamijo proposed an accident recognition system based on the Hidden Markov Model (HMM) [6] This system can learn various event patterns and identify current events. Compare with Multi Forward Neural Network with Back-Propagation Algorithm, the results show the proposed system (FLCS) has lower false detection rate and shorter mean detection time [9]. Kamino proposed a traffic monitoring system based on the ST-MRF model for detecting slow vehicles, traffic congestion and incidents [10]. This system detects and assesses abnormal traffic events using a semantic hierarchy algorithm containing three classes: coordinate-class, behavior-class and event-class, which are designed to be robust against occlusion and illumination changes. The vision-based approach for incident detection has advantages over traditional loop data techniques in terms of flexibility, and ease of installation and maintenance
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