Research On Video-based Element Statistics And Analysis

Advertisements,as an important means of propagating an item or a concept,is widely seen in people's daily lives;whether it is propaganda of articles or propaganda of ideas,it would bring certain commercial value to the society or spread to people a type of culture,therefore,a high-quality advertisement would always help the development of society.With the development of social economy,the traditional forms of advertisements such as newspapers and radios have gradually been replaced by online advertisements.Online advertising,also known as Internet advertising,refers to advertising placed on online media.Unlike traditional advertising,online advertising has formed a crowd-oriented and product-oriented technology delivery mode in the past two decades.At the same time,the product form and business logic of online advertisements are more complicated.If advertising developers want to have macro control over the creation of online advertisements,they must explore from two directions.Firstly,the connotation of advertisements,that is,the definition and purpose of the advertisements business activities.Secondly,the extension of advertisements,namely,the key product form generated in the development of online advertising^[1].Therefore,connotation and extension have become synonymous with high-quality advertisements creation,the demand of customers and the orientation of products have become key influencing factors of high-quality advertisements.In the era of big data,all analysis methods are based on high-quality statistics.Therefore,this paper proposed a method for advertisements element statistics for indoor video,and obtains and counts the frequency of each element in video ads.Information;this method mainly involves the inter-frame difference algorithm,the optical flow estimation algorithm and the target detection algorithm in deep learning;this paper applies these three algorithms from the following three aspects.?1?Proposing the video sampling analysis in different application scenarios;analyzing the two video sampling algorithms in the supplementary training set and service by comparing the inter-frame difference algorithm and the SimpleFlow optical flow algorithm recall rate,accuracy and running time.This paper analyzes the application prospects of two video sampling technologies in the two scenarios of supplementary training set and server-side video sampling.?2?Proposing the identification model of advertisements elements under different requirements;this paper puts forward three kinds of requirements in the process of constructing the advertisements elements recognition model,which are the model with both detection speed and accuracy,the model with faster detection speed and the accurate detection.For the three types of requirements,this paper takes the two types of backbone networks,DarkNet and ResNet,as the main body.,and improves the structure of the anchor box and Multi-scale detection layer of the yolo-v3 target detection model in deep learning to realize three types of requirements.Implementing the statistical model of advertising elements under three types of requirements.?3?Proposing the implementation of the statistical platform for home advertisements elements;using the front-end framework Bootstrap and the server-side framework Spring-Boot to build a home advertising element statistics platform,users can not only directly view the pie chart statistics of the advertising elements stored in the server at various time periods.As a result,new video uploads can also be added,the results of the advertisement element statistics are refreshed by key frame extraction and the advertisement recognition model.