Traffic flow detection method based on fusion of frames differencing and background differencing

In complex urban road conditions, stationary or illegally parked vehicles present a considerable risk to the overall traffic system. In safety-critical applications like autonomous driving, the detection of stopped vehicles is of utmost importance. Previous methods for detecting stopped vehicles have been designed for stationary viewpoints, such as security cameras, which consistently monitor fixed locations. However, these methods for detecting stopped vehicles based on stationary views cannot address blind spots and are not applicable from driving vehicles. To address these limitations, we propose a novel deep learning-based framework for detecting stopped vehicles in dynamic environments, particularly those recorded by dashcams. The proposed framework integrates a deep learning-based object detector and tracker, along with movement estimation using the dense optical flow method. We also introduced additional centerline detection and inter-vehicle distance measurement. The experimental results demonstrate that the proposed framework can effectively identify stopped vehicles under real-world road conditions.

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Traffic flow detection method based on fusion of frames differencing and background differencing

Cited by 2 publications

References 2 publications

An accurate vehicle counting approach based on block background modeling and updating

An accurate vehicle counting approach based on block background modeling and updating

Deep Learning-Based Stopped Vehicle Detection Method Utilizing In-Vehicle Dashcams

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