Image analysis and deep learning-based number of people statistical method

Abstract

An image analysis and deep learning-based number of people statistical method disclosed by the present invention comprises the following steps of A carrying out the pyramid model calculation on an input image, and generating the images of a plurality of resolutions and sizes; B sliding the windows on each layer of a pyramid, calculating the HOG feature values of the window areas, classifying via a linear support vector machine (SVM) classifier, and determining whether the windows are the head-shoulder areas; C for each head-shoulder area given out in the step B, extracting the corresponding image, normalizing to a set same size, and inputting in a deep neural network to obtain the classification output; D carrying out the non-maximum suppression on the all head-shoulder windows outputted in the step C to merge the overlapped detection results of the adjacent areas and scales. The image analysis and deep learning-based number of people statistical method of the present invention can improve the insufficiency of the prior art, and can realize a higher number of people statistical performance with the faster speed.

Description

technical field [0001] The invention relates to the technical field of image target recognition and deep learning, in particular to a method for counting people based on image analysis and deep learning. Background technique [0002] Using computer vision technology to count the number of people in surveillance images or videos can be widely used in project scenarios such as stampede warning, traffic guidance, shop people flow assessment, and attendance rate statistics. However, existing people counting systems often have large errors in crowded environments. This is because there are usually a lot of occlusions in a crowded environment, resulting in the fact that the features of the parts below the shoulders of the human body can hardly be used reliably and effectively. However, if only feature extraction and positioning are performed on the head and shoulders, since the shape curve of the head and shoulders is relatively simple, traditional hand-designed feature extractio...

AI Technical Summary

Problems solved by technology

However, due to the large amount of calculation and slow speed, it has not been widely used in monitoring scenarios with high real-time requirements.

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