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

A technology of support vector machine and multi-instance learning, which is applied in character and pattern recognition, computing, computer parts, etc., can solve the problems of large differences and low recognition rate of direct matching

Active Publication Date: 2014-06-04
HUZHOU TEACHERS COLLEGE
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  • Abstract
  • Description
  • Claims
  • Application Information

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

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

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Embodiment Construction

[0019] The present invention is applied to a multi-core support vector machine multi-instance learning algorithm for pedestrian re-identification, comprising the following steps:

[0020] a) Multi-feature description:

[0021]a1) Color feature: The color feature is extracted according to the following method. First, the pedestrian image is divided into five areas of equal size. Each area extracts the histogram of the three components of H, S, and V, and the interval is 10. The extracted area The features are concatenated to finally form a global feature whose feature is a 150-dimensional column vector. The purpose of region division is to preserve the local information of the image and prevent the mismatch of the same color in different regions;

[0022] a2) SIFT feature extraction and word bag construction: SIFT features are extracted according to the 4×4 template. Since the SIFT features of the image are only local feature descriptions, it is necessary to use the word bag mo...

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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...

Claims

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Application Information

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IPC IPC(8): G06K9/66G06K9/46
Inventor 蒋云良刘红海侯向华黄旭
Owner HUZHOU TEACHERS COLLEGE
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