基于卷积神经网络的视频大数据智能预警分析

Intelligent Early Warning Analysis of Video Dig Data Based on Convolutional Neural Network

摘要:

大数据时代的到来对电力视频监控应用提出了新的要求，现有的电力视频监控系统基本只承担了远程录像 机的作用，视频监控系统在主动预警方面的能力仍然没有体现，变电站仍然不够“智能”。基于卷积神经网络技术，先 从现有的视频监控平台中提取出原始视频素材，利用视频云存储平台进行存储和 video2pic 工具进行数据清洗; 然后 将现场隐患行为进行分类分析并标记; 最后通过特征提取、分类器模型训练和验证，实现对现有视频监控智能化升 级。通过对视频大数据的挖掘实现对现场情况的实时智能预警分析，实现对变电站违章作业和营业厅不规范服务行 为的自动挖掘、实时预警以及智能推送，摆脱对人工值守的依赖，在降低人力投入的同时提升电力生产安全管控能力 和优质服务水平，使电力视频监控系统能充分发挥其“监”与“控”的作用。

Abstract:

The arrival of big data era puts forward new requirements for the application of power video surveillance． The power video monitoring system nowadays only plays the role of remote video recorder basically． The capability of video monitoring system in active early warning is still not used，and the substation is still not " intelligent enough" ． Based on the convolutional neural network technology，the original video material is extracted from the existing video monitoring platform． The video cloud storage platform is used for storage and video2pic is used for data cleaning． Then people's unsafe behavior of the scene is classified，analyzed and marked． Finally，the intelligent upgrade of the existing video monitoring is realized through feature extraction，classifier model training and verification． Through the mining of large video data，real － time intelligent early warning analysis of the situation on the spot is realized，and automatic mining，real － time early warning and intelligent push of irregular operation and business hall service behavior of substation are realized． By getting rid of the dependence on manual duty，saving labor investment and improving power production safety control capacity and quality service level，the power video monitoring system can give full play to its " supervision" and " control" role．