A fast pedestrian detection method for complex public scenes based on depth learning

Abstract

The invention relates to a fast pedestrian detection method for complex public scenes based on depth learning, wherein the method includes pixel size preprocessing of a training image and a test image, pre-training of a convolution neural network based on classification tasks, pedestrian detection training of the convolution neural network based on pedestrian detection tasks, use of threshold filter to eliminate prediction boxes with low confidence, and use of non-maximum inhibition to eliminate multiple prediction of the same pedestrian. Cross entropy is used as loss function in pre-training.Finally, the improved mean square error is used as the loss function to make the network output the regression results of predicting the location of pedestrians. In the testing phase, the image is used as the input of the convolution neural network, and the threshold filter and non-maximum suppression are used to filter all the output prediction results of the convolution neural network, so thatthe pedestrian location information can be detected and the pedestrian intelligent monitoring can be realized.

Description

technical field [0001] The invention relates to image processing technology, in particular to a method for fast pedestrian detection in public scenes based on convolutional neural networks. Background technique [0002] In recent years, surveillance cameras have been used in various public places. Public scenes such as airports, stations, hospitals, and roads have covered thousands of surveillance cameras to detect abnormal behaviors of pedestrians in public scenes for analyzing and discovering the flow of people. , it is of great significance to track specific groups of people. Due to the huge amount of video data and many pedestrians, it is often difficult to quickly and accurately analyze the target pedestrians by relying on manual analysis. However, some existing automatic pedestrian detection methods are often slow and cannot complete real-time monitoring of pedestrian targets. In order to realize the automatic real-time detection of pedestrians in public scenes, it i...

AI Technical Summary

Problems solved by technology

Due to the huge amount of video data and many pedestrians, it is often difficult to quickly and accurately analyze the target pedestrians by relying on manual analysis.

However, some existing automatic pedestrian detection methods are often slow and cannot complete real-time monitoring of pedestrian targets.

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