Multi-feature fusion overhead pedestrian detection method based on aggregated channel features and a gray level co-occurrence matrix

Abstract

The invention relates to a multi-feature fusion overhead pedestrian detection method based on aggregated channel features and a gray level co-occurrence matrix. The method comprises extracting ACF features of a plurality of aggregation channels in a sample training set, obtaining aggregation channel feature vectors and gray level co-occurrence matrix feature vectors, sending the two vectors to a soft cascade Adaboost classifier for training, and obtaining classifier 1 and classifier 2; reading an image to be measured, extracting ACF features of the image to be measured, and obtaining an aggregation channel feature vector; sending feature vectors of aggregation channels into a classifier to classify, and obtaining candidate coordinates and target windows. The eigenvector of gray level co-occurrence matrix is obtained and sent to classifier 2 to eliminate background interference, and the output result of the final target is obtained. As that color, the gradient direction histogram, the gradient and the texture feature are fused, the background similar to the human head is filter out, the missed detection and the false detection rate of the classifier are effectively reduced, and thedetection performance of the pedestrian overlooking when a plurality of interference backgrounds exist is improved, and the method is stable, reliable and efficient, and has strong practical application value.

Description

Technical field [0001] The invention belongs to the technical field of computer vision pedestrian detection, and particularly relates to a multi-feature fusion overhead pedestrian detection method based on aggregation channel features and gray-level co-occurrence matrix. Background technique [0002] At present, the overlooking pedestrian detection technology in intelligent monitoring scenarios is widely used in pedestrian flow statistics and pedestrian intelligent analysis to achieve effective supervision of crowded places. Pedestrian detection based on bird's eye view has made rapid progress in recent years. However, the pedestrian's hairstyle, hair color and complex surrounding background increase the difficulty of detection, resulting in the detection effect is still not ideal, and further improvement is needed. There are the following categories of overhead pedestrian detection methods. The first type is a method based on shape analysis. The main idea is to filter the imag...

AI Technical Summary

Problems solved by technology

Although the current overhead pedestrian detection method based on multi-feature extraction can reduce the false detection of overhead pedestrian detection in the background of interference, the degree of improvement in algorithm performance is limited, and the combination of multiple single features will also increase the computational complexity.

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