Multi-kernel support vector machine multi-instance learning algorithm applied to pedestrian re-identification

Abstract

The invention discloses a multi-kernel support vector machine multi-instance learning algorithm applied to pedestrian re-identification. The algorithm includes two main steps, namely multi-feature description and a multi-kernel SVM model multi-instance learning algorithm. According to the algorithm, HSV color features and SIFT local features of two pictures, taken under a camera A and a camera B, of the same pedestrian are extracted to construct a word bag, and difference vectors of the two kinds of the features represent the conversion relation under the two cameras to serve as two instance samples and are encapsulated as a bag; then a multi-kernel support vector machine model is optimized, the bag is trained by means of linear fusion of the Gaussian kernel and a polynomial kernel, optimal parameters are obtained through multi-instance learning, and a high identification rate is achieved.

Description

【Technical field】 [0001] The invention relates to the technical field of pedestrian re-identification algorithms, in particular to the technical field of multi-core support vector machine multi-instance learning algorithms applied to pedestrian re-identification. 【Background technique】 [0002] With the launch of the safe city strategy, more and more surveillance cameras have been installed in traffic fortresses. These traffic fortresses are far away, and it is difficult to use traditional single cameras for tracking. Pedestrian re-identification refers to the matching of pedestrians under the monitoring of non-overlapping multi-cameras, that is, how to confirm whether the targets detected by cameras in different positions at different times are the same person. Because the imaging of the camera is affected by factors such as parameters, lighting conditions, angles, backgrounds, etc., the same target captured by different cameras is quite different. [0003] Re-identificati...

AI Technical Summary

Problems solved by technology

[0004] The description method relies on stable features, and the direct matching recognition rate of a single feature is low. Only the fusion of multiple features can guarantee the recognition effect

However, the current metric learning method mainly focuses on learning and matching of a single feature, and the appearance of the same person captured by different cameras may have huge changes. Back or side, the difference is large. Obviously, a single feature to describe a pedestrian's front, side, and rear images has great limitations

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