Method for realizing portrait clustering based on multi-data fusion analysis

Abstract

The invention discloses a method for realizing portrait clustering based on multi-data fusion analysis, which comprises the following steps of: accessing required video stream data and face and human body snapshot picture data through an access data module; respectively calling a face video structuralization and picture structuralization calculation engine for the video stream data and the human body snapshot picture data to carry out face and human body structuralization; calling a face clustering analysis algorithm, and performing aggregation association on the same faces through fusion analysis of places, time, structured feature values and structured attributes to complete one-time portrait clustering analysis; creating and endowing a video identity ID through a video identity ID management module; and calling a human body correlation analysis algorithm to complete correlation analysis of the suspected human body data. According to the method, on the basis of face feature value data comparative analysis, time-space relation analysis is carried out by fusing the equipment basic information and the structured attribute data, so that the data clustering precision is greatly improved, and the aggregation divergence is reduced.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a method for realizing portrait clustering based on multi-data fusion analysis. Background technique [0002] Face clustering technical solutions in the prior art mainly have the following two implementations: [0003] 1. Based on a built-in static face database feature database, real-time feature value comparison is performed on the connected face snapshot data, and the captured pictures that meet the set threshold will be directly linked to the relevant personnel. The shortcomings of this solution are: first, it relies too much on the existing static face database, and when static data support cannot be provided, the clustering effect is extremely general; second, it does not consider the fusion analysis of other data. [0004] 2. A clustering analysis algorithm for face snapshot data, but only for similarity comparison of face structural feature values, and clustering and m...

AI Technical Summary

Problems solved by technology

The disadvantages of this scheme are: first, it relies too much on the existing static face database, and when static data support cannot be provided, the clustering effect is extremely general; second, it does not consider the fusion analysis of other data

The disadvantage of this scheme is: with the rapid growth of the access data volume and the archive clustering cardinality, the archive clustering error will be amplified, the clustering accuracy will decrease and the divergence will increase

[0005] There are also common problems in the above two technical solutions: first, the introduction of more auxiliary analysis data is not considered to improve the clustering accuracy; In the data environment, the accuracy of archive clustering is poor

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