A Pedestrian Detection Method Based on Deep Learning Technology

Abstract

The invention discloses a pedestrian detection method based on deep learning technology. Firstly, based on migration learning, a "gradual migration" strategy is used to train a binary classification model to initialize the final model parameters; then, the currently very popular and efficient Faster RCNN framework is adopted and improved To complete the pedestrian detection work, which is based on CNN features, not only can process images of any scale, but also has a fast detection speed. Compared with the published invention patents, the method disclosed in the present invention does not require a special design of the network, fully utilizes the existing available data, and can still achieve good experimental results by using a general network structure, giving full play to the deep volume. Based on the advantages of the product network, it has the advantages of simple design, good robustness, high detection accuracy and low missed detection rate.

Description

technical field [0001] The invention relates to a pedestrian detection method based on deep learning technology, which belongs to the technical field of image processing and computer vision. Background technique [0002] With the advancement and development of science and technology, our way of life is also slowly changing. Many tasks that used to require a lot of manpower can now be completed by computers. In recent years, due to the rapid development of Internet technology, communication technology, and Internet of Things technology, a large amount of video image information has been generated, and these massive amounts of information contain huge information volume and commercial value. [0003] An important source of human perception of the world is through visual information. Studies have shown that about 80% to 90% of the information that humans obtain from the outside world comes from the visual information obtained by human eyes. Human beings have a high ability to ...

AI Technical Summary

Problems solved by technology

This method also has obvious deficiencies: (1) It is necessary to pre-normalize the image to a specific size, so that the aspect ratio and scale of the input image are ignored. When the obtained model is used for an image of any size, it is often necessary to crop the original image or do some geometric transformation, which will affect the accuracy and robustness of the model due to the loss of a large amount of useful information or the introduction of geometric distortion. ; (2) The neural network structure adopted by this method is very simple, and the information contained in the data cannot be well mined

However, this method also has some shortcomings: (1) the original image needs to be pre-cropped, which is not only labor-intensive, but also some useful information cannot be used; (2) the original image needs to be specially pre-processed, and the process is relatively Complicated; (3) The data is relatively limited and single, and only one database (such as Caltech, ETH) is used, and the available data is not comprehensively utilized; (4) Although the network has been specially designed (such as components, occlusion design), but Generally speaking, it is not deep enough, it is difficult to mine the deep information of the data, and it does not give full play to the characteristics of CNN features such as strong robustness and wide applicability

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