Vision-based detection of vehicle line-pressing violation has been widely used in intelligent transportation system. However, the accuracy of most current methods is low due to weather, illumination, shooting angle, etc., which increases the workload of manual verification. To cope with these problems, an efficient automatic verification method of vehicle line-pressing violation is proposed based on convolutional neural network (CNN) and geometric projection. First, a CNN model is used to detect vehicles and features are extracted to match the vehicles with line-pressing violation. Second, based on driving direction and vehicle 2D bounding box, pose relationship of the vehicles to the ground is fitted. Finally, according to the relationship between lane line and vehicle chassis range, automatic verification of vehicle line-pressing violation is realized. In the experimental results, the recall rate and accuracy rate reached 90.3 and 99.7% respectively in 200 test samples, which is better than those of current methods. The experimental results show that the proposed method has high accuracy, recall rate and good performance, and can be used for the automatic verification of vehicle line-pressing violation.
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This work is being supported by the National Key Research and Development Project of China under Grant No. 2020AAA0104001, the Zhejiang Provincial Science and Technology Planning Key Project of China under Grant No. 2021C03129 and the Zhejiang Lab. under Grant No. 2019KD0AD011005.
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Gao, F., Zhou, M., Weng, L. et al. An automatic verification method for vehicle line-pressing violation based on CNN and geometric projection. J Ambient Intell Human Comput (2021). https://doi.org/10.1007/s12652-021-03400-9
- Vehicle re-identification
- Vehicle line-pressing detection
- Feature fusion
- Traffic violation