Abstract
Real-time traffic monitoring is essential in many novel applications, from traffic management to smart navigation systems. The large number of traffic cameras being integrated into urban infrastructures has enabled efficient traffic monitoring as an intervention in reducing traffic accidents and related casualties. In this paper, we focus on the problem of the automatic detection of anomalous driving behaviors, e.g., speeding or stopping on a bike lane, by using the traffic-camera feed that is available online. This can play an important role in personalized route-planning applications where, for instance, a user wants find the safest paths to get to a destination. We present an integrated system that accurately detects, tracks, and classifies vehicles using online traffic-camera feed.
Highlights
Traffic management aims at reducing traffic incidents and improving traffic flow
To create an integrated system that can be used to detect anomalous driving behaviors based on traffic footage, we performed vehicle detection, tracking, and anomalous-driving-behavior classification using the steps as explained below
Several experiments were carried out to evaluate the performance of the proposed system in identifying anomalous driving behaviors
Summary
Traffic management aims at reducing traffic incidents and improving traffic flow. Malicious or unintentional anomalous driving behaviors, as categorised and shown, can directly or indirectly result in traffic incidents and affect transport efficiency [1]. Knowing where, when, and how often these behaviors happen is highly valuable both to road users, e.g., to adjust their path through safer routes, and road authorities, e.g., to improve signage. Traditional hardware-based detection techniques, such as inductive loop detectors, laser detectors, and optical detectors, are expensive to install and maintain [2]. These techniques have limited functionalities when it comes to analyzing the behavior of drivers and other road users. An inductive loop detector cannot distinguish a cyclist from a car
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