Pedestrian detection method

Abstract

The invention discloses a pedestrian detection method. Multiple times of convolution and pooling are performed on an input image through the pedestrian detection method based on a convolutional neuralnetwork; the features of the original image are extracted so as to obtain the corresponding feature graph of the original image; the corresponding feature graph after zooming of the original image isapproximately calculated through the image feature pyramid rules; a candidate window is generated through a region proposal network RPN; a candidate proposal window is further selected and summarizedaccording to the pedestrian size distribution in the candidate window; the corresponding weight of different scales of pedestrian targets on different scales of images is trained by using the training data having the tag; and the classifier network is trained. The summarized candidate window is solved, and the confidence obtained through the classifier and the set threshold are compared and finalpedestrian detection judgment is performed. Heavy calculation amount of obtaining the feature graph through image zooming calculation can be avoided by application of the image feature pyramid, and detection is performed on different feature graphs by using the weighing mode of different weights so that misjudgment and leak detection caused by single feature graph detection can be effectively avoided.

Description

technical field [0001] The invention relates to a pedestrian detection method and belongs to the field of target detection. Background technique [0002] In recent years, pedestrian detection technology has been widely used in intelligent monitoring, automatic driving, robot vision, etc. In practical applications, pedestrians' clothing, posture, and especially the size of pedestrians captured in videos are variable, making pedestrian detection a great challenge. There are two main methods for pedestrian detection: one is the traditional pedestrian detection method based on sliding windows, and the other is the pedestrian detection method based on deep learning to extract features. The traditional pedestrian detection method has a large amount of calculation and does not use GPU resources to limit the detection speed. Due to the continuous improvement of computer performance and the use of GPU computing power, most deep learning methods based on learning features are faster ...

AI Technical Summary

Problems solved by technology

[0003] In order to solve the difficult trade-off between speed and detection accuracy in the pedestrian detection process

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