A method of group social relationship mining based on video data

Abstract

The present invention provides a method for mining group social relationships based on video data, the steps of which are as follows: 1. Preprocessing images of people in the video; The group relationship corresponding to the graph; 3. Analysis and prediction of the group relationship; 4. Analysis of the action expression and state of a single person; through the above steps, the present invention mines data from the video data material with richer content, and can solve video In order to effectively quantify and evaluate the multi-person relationship in various scenes; the present invention supports real-time analysis of various real-life scenes in the future, and can analyze the relationship between characters in multi-person complex scenes. Analysis provides strong methodological support.

Description

Technical field: [0001] The invention proposes a method for mining group social relations based on video data. It involves face detection, recognition, network structure analysis, calculation of social relations, etc., and belongs to the field of data mining. Background technique: [0002] Social relationship mining is a fast-growing field and an interdisciplinary subject that deeply integrates computer science and social science. Generally, the relationship between people is difficult to obtain through social survey methods such as direct questionnaires. It is necessary to use the current big data methods to indirectly mine social relationships. Social relationship mining has a wide range of applications in technology management, business intelligence, emergency management, anti-terrorism and anti-riot and other aspects. [0003] "Social connections are essential to finding meaning in life". By establishing and maintaining connections with others, individuals can achieve...

AI Technical Summary

Problems solved by technology

[0009] To sum up, the current research and algorithms are mostly based on data types such as quantitative characteristics or language analysis, and carry out indirect social relationship mining and analysis, but the level of social connections reflected in these data is relatively superficial, and most of them are medium- and long-term social relationships. The accumulation of static relationships (such as address book data), or the embodiment of similarity in life patterns (such as track data), or the social interaction under a certain interest (such as Weibo or blog), is difficult to reflect in a multi-type, multi- Deep features of social relationships in groups of interests and multiple behavioral patterns

Images