Pedestrian real-time detection method based on neural network

Abstract

The invention discloses a pedestrian real-time detection method based on a neural network, and the method comprises the steps: 1) collecting data, and constructing an original training data set; 2) calibrating pedestrian positions corresponding to the acquired images for the images in the original training data set, and constructing real position output of the training data set; 3) constructing aneural network model; 4) in order to increase the network training data volume and enhance the applicability of the method, performing data enhancement on the original input image by using a data enhancement method; 5) setting training parameters for the designed neural network model for training, and storing the trained neural network model parameters; and 6) acquiring image data of pedestrian detection to be performed by using an image acquisition device, and then inputting the image of the pedestrian to be detected into the stored neural network model to obtain the pedestrian position of the image of pedestrian detection to be performed. The method can reduce a large amount of detection time on the premise of meeting the detection accuracy of pedestrian detection.

Description

technical field [0001] The invention relates to the technical field of image pattern recognition, in particular to a neural network-based real-time pedestrian detection method. Background technique [0002] In the field of computer vision, pedestrian detection refers to the detection of the position of pedestrians based on the image or video information collected by the camera. Pedestrian detection is of great significance, and it is the first step in applications such as vehicle assisted driving, intelligent video surveillance, and human behavior analysis. Due to the increasing demand for pedestrian detection in public security, digital entertainment industry and other fields, pedestrian detection technology has attracted more and more attention from academia and industry. Pedestrian detection has a wide range of application scenarios, such as people flow statistics at entrances and exits of important passages, building access control systems, security protection, etc. ...

AI Technical Summary

Problems solved by technology

By acquiring a large amount of image data, training a deep neural network model, and then performing recognition, the accuracy rate is high and the robustness is strong. However, the deep learning method often has a large amount of calculation, requires a long pedestrian detection time and powerful hardware conditions, and is specific to applications. The scene cannot be satisfied

The above limitations make the current pedestrian detection method based on traditional image processing feature extraction less robust, and it is difficult to use deep learning for real-time and effective pedestrian detection

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