A Pedestrian Recognition Method Based on Sequential Deep Belief Network

Abstract

The invention discloses a pedestrian recognition method based on a sequence deep belief network, comprising the following steps: preprocessing the training images in the pedestrian database, obtaining training sample images, performing HOG feature extraction on the obtained training sample images, constructing and training Based on the sequence deep belief network of the sequence restricted Boltzmann machine, the obtained HOG features are further extracted with the sequence deep belief network to form the feature vector of the training sample, and the obtained feature data is input into the support vector machine classifier to complete Training; preprocessing the pedestrian image to be tested to obtain a test sample, using HOG and sequence deep belief network to extract pedestrian features from the test sample to form the feature vector of the test sample; input the feature vector of the test sample into the support vector machine classifier, Recognize whether the test image is pedestrian or non-pedestrian. The invention can obtain better classification performance, improve the accuracy rate of pedestrian identification, and enhance the robustness of pedestrian identification algorithm.

Description

Technical field [0001] The invention belongs to the technical field of computer vision, and specifically relates to a pedestrian recognition method based on a sequence deep belief network. Background technique [0002] Pedestrian recognition has a wide range of application prospects in intelligent transportation systems and intelligent monitoring systems, but it is still an open problem in the field of computer vision. The reason is that the appearance of pedestrians and the background environment, such as clothing, posture, lighting, and viewing angles, have changed greatly. In addition, the background is complex, and the recognition accuracy is not high. [0003] In the entire pedestrian detection system, feature extraction technology is the most basic and most critical step. At present, some scholars' research on pedestrian recognition and classification is mainly focused on feature extraction. The main features used for pedestrian detection include gradient histogram features,...

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 the characteristics of redundant information, high dimensionality and poor discrimination ability; harr is discontinuous and is mostly used in face recognition, but it is not effective for pedestrian recognition; The dimensionality of the local feature SIFT is very high and there is a deviation in the rotation invariance; the MSER feature detects fewer feature points, etc.

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