Sequence deeply convinced network-based pedestrian identifying method

Abstract

The invention discloses a sequence deeply convinced network-based pedestrian identifying method. The method comprises the following steps of preprocessing a training image in a pedestrian database to obtain a training sample image, extracting an HOG (Histograms of Oriented Gradients) feature from the obtained training sample image, building and training a sequence restricted Boltzmann machine-based sequence deeply convinced network, using the sequence deeply convinced network to further extract features from the obtained HOG feature to form a feature vector of the training sample, inputting the obtained feature data into a support vector machine classifier, and finishing training; preprocessing a to-be-tested pedestrian image to obtain a test sample; using an HOG and the sequence deeply convinced network to extract pedestrian features from the test sample to form a feature vector of the test sample; inputting the feature vector of the test sample into the support vector machine classifier, and identifying whether the test image is a pedestrian or not. According to the method, better classification performance can be obtained, the accuracy of pedestrian identification is improved, and the robustness of a pedestrian identifying algorithm is enhanced.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and in particular relates to a pedestrian recognition method based on a sequence deep belief network. Background technique [0002] Pedestrian recognition has broad application prospects in intelligent transportation systems and intelligent monitoring systems, but it is still an open problem in the field of computer vision, because pedestrian appearance and background environment, such as clothing, posture, lighting, viewing angle, etc., vary greatly. Coupled with the complex background, the recognition accuracy is not high. [0003] In the entire pedestrian detection system, feature extraction technology is the most basic and critical step. At present, some scholars' research on pedestrian recognition and classification mainly focuses on feature extraction. The main features used for pedestrian detection are gradient histogram features, LBP features, edgelet features, harr features, etc...

AI Technical Summary

Problems solved by technology

The gradient direction histogram HOG descriptor has been proved by experiments to be the best performance among single features at this stage, but its HOG descriptor generation process is lengthy, resulting in slow speed and poor real-time performance; and it is difficult to deal with occlusion problems

Some other features also have their shortcomings. For example, LBP has

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