Pedestrian early warning method and system for freeway entrance and exit

Abstract

The invention discloses a pedestrian early warning method and system for freeway entrance and exit. The method comprises the steps of collecting a pedestrian data set, making sample tag files as training samples, and extracting features of pedestrians from the training samples by using a convolutional neural network method; according to the extracted pedestrian features, iteratively training a region proposal network and a target detection network to obtain a pedestrian detection network model; and shooting road condition videos in real time, transmitting the videos to a trained pedestrian detection network model for obtaining target probabilities and target boxes of the pedestrians, immediately giving an alarm and prompting pedestrian position information. The quantity and the geographicpositions of the pedestrians are determined while real-time pedestrian detection is performed; and under the remote condition, the detection speed is high and the accuracy is high, so that the freewayemergency event processing efficiency is improved, the introduction of additional errors can be avoided, and the processing accuracy is ensured.

Description

technical field [0001] The invention relates to the field of video object detection, in particular to a pedestrian early warning method and system for expressway entrances and exits. Background technique [0002] As the severity of expressway accidents in our country increases year by year, it has now risen to the second place in the world. For every 11 deaths in traffic accidents, one person died on the highway, and the proportion of casualties in road traffic accidents increased year by year. The fatality rate and fatality ratio of expressways are 15.4% and 22.2% higher than national highways respectively. Therefore, it is necessary to accurately grasp the traffic operation status and detect abnormal situations in time for traffic management and emergency response strategies. [0003] One of the keys to reduce the rate of highway traffic accidents is to ensure that there are only vehicles on the highway. If someone accidentally enters the highway without noticing a pote...

AI Technical Summary

Problems solved by technology

At present, the traffic abnormal state discrimination algorithms at home and abroad mainly include statistical algorithms, smoothing filter algorithms, catastrophe theory and artificial intelligence event detection algorithms, etc. Among them, the portability of statistical algorithms, smoothing filter algorithms and catastrophe theory algorithms is relatively poor, and the results are not good. satisfactory

Artificial intelligence algorithms have been greatly developed in recent years, especially the upsurge of deep learning research has swept over the past two years. However, the current deep learning method applied to the division of expressway traffic operation status is not detailed enough, and due to the expressway's speed High specificity and accuracy requirements restrict the development of intelligent transportation systems

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