A vehicle object multi-level knowledge dictionary construction method for monitoring video compression

Abstract

The invention relates to a vehicle object multi-level knowledge dictionary construction method for monitoring video compression, which is used for expressing and compressing objects in a monitoring video, and comprises the following steps of extracting texture layer knowledge which comprises common textures of different types of vehicles in a multi-source monitoring video, and modeling the texturelayer knowledge by using a texture dictionary; extracting structural layer knowledge which comprises a common structure of the same type of vehicles, and modeling the structural layer knowledge by using a three-dimensional model; and extracting the personalized layer knowledge, wherein the personalized layer knowledge comprises transient knowledge that a certain vehicle individual keeps stable for a period of time, and using a residual dictionary to model the vehicle individual. According to the method, the multi-level knowledge dictionary of the object is established from the three levels ofthe texture layer, the structure layer and the personality layer, the appearance of the moving object in the image in the complex environment can be better expressed, the information such as edge details is richer, the tracking recognition efficiency of the monitored vehicle can be improved, and the method can be widely applied to the aspects of intelligent transportation and the like.

Description

technical field [0001] The invention belongs to the field of surveillance video coding, and in particular relates to a method for constructing a vehicle object multi-level knowledge dictionary oriented to surveillance video compression, which is used to generate prediction references for moving objects in the surveillance video and improve the coding efficiency of the surveillance video. Background technique [0002] In recent years, the metropolitan surveillance network has become an indispensable urban infrastructure, playing an important role in the fields of intelligent transportation, smart city construction, fighting crime, and maintaining public safety. However, with the increase in the number of surveillance cameras and the high-definition trend of surveillance videos, the daily surge of massive surveillance video data poses a severe challenge to video compression efficiency. [0003] Existing surveillance video compression mainly uses a hybrid coding framework based...

AI Technical Summary

Problems solved by technology

However, 3D models usually do not consider physical factors such as artificial decoration and environmental factors such as lighting when modeling. This type of coarse-grained knowledge is quite different from objects in real videos. Only using 3D models to predict objects will lead to Larger prediction residuals reduce coding efficiency

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