一种新的结合卷积神经网络的隧道内停车检测方法

In order to more accurately detect the vehicle stopping in highway tunnels, this paper proposes a new methodology that combines the traditional image processing technology with deep learning. Firstly, the foreground moving targets are extracted using the background difference method based on Gaussian mixture model (GMM). Then the meanshift algorithm is applied to track these foreground moving targets. By calculating the speed of the moving targets and the correlation of the moving targets between the neighboring video frames, and comparing the results with the speed threshold and correlation threshold, the static target is detected. Finally, combined with the convolutional neural network (CNN) classification model, whether the static target is vehicle is identified. The method proposed in this paper is validated using the real highway tunnel vehicle stopping video and achieves an accuracy of at least 84%. Compared with the traditional image processing method without CNN, our method improves at least 63% accuracy.