Pedestrian recognition system and pedestrian recognition processing method based on random forest support vector machine

Abstract

The invention relates to a pedestrian recognition system based on a random forest support vector machine. The pedestrian recognition system comprises a characteristic extraction module, a clustering module, a random forest creating module and a scoring model module. The invention also relates to a pedestrian recognition processing method based on the random forest support vector machine. A similarity ranking way is used for replacing the comparison of traditional similarity absolute values, a threshold value does not need to be delimited, and an obtained ranking result is convenient for users to judge; and since multiple characteristics are required for establishing a random forest model and samples can not be subjected to mutual classification perfection only from apparent characteristics, a K-means clustering algorithm is adopted to replace a phenomenon that a sample category is manually given, and potential relationships among samples can be mined. The method and the system exhibit robustness on pedestrian posture change and can eliminate interferences from other types of samples when the similarity is calculated, a ranking result of RankSVM (Support Vector Machine) is in the top, and recognition accuracy can be improved when the similarity is calculated. Compared with traditional algorithms including MCC, RankSVM and the like listed in the prior art, the pedestrian recognition system is high in recognition accuracy.

Description

technical field [0001] The invention relates to the technical field of pedestrian recognition for intelligent monitoring, in particular to a pedestrian recognition system and processing method based on a random forest support vector machine. Background technique [0002] Pedestrian recognition is one of the active research directions in the field of pattern recognition. In pedestrian retrieval and recognition, with the increase of the sample database, the speed and accuracy of retrieval and recognition of an image are greatly affected. In terms of pedestrian feature extraction, color histogram information such as RGB and HSV are widely used, but they are easily affected by the environment. Gabor wavelet extracts pedestrian texture features, but when the accurate boundary curve cannot be extracted, the final texture features will change greatly. The texture features extracted by LBP are robust to illumination, but when the pedestrian pose changes greatly, the accuracy of id...

AI Technical Summary

Problems solved by technology

In terms of pedestrian feature extraction, color histogram information such as RGB and HSV are widely used, but they are easily affected by the environment.

Gabor wavelet extracts pedestrian texture features, but when the accurate boundary curve cannot be extracted, the final texture features will change greatly

The texture features extracted by LBP are robust to illumination, but when the pedestrian pose changes greatly, the accuracy of identifying pedestrian targets only from the texture features extracted from LBP will be very low.

In addition, in terms of similarity calculation, with the increase of the sample library, the negative samples faced by the test image increase, and the probability of occurrence of samples with similar characteristics to the test image increases, which will affect the accuracy of the test results. Even if RankSVM The ranking order of similarity is calculated, and the absolute value of similarity is not given, but the ranking result is for users to judge by themselves. As the sample size increases, the probability of interference samples will increase, and the ranking order of positive samples will also be lower.

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