Research On Key Techniques In Coalmine Intelligent Video Surveillance System

At present most of our coalmine video surveillance system is still at the stage ofmanual monitoring, intelligent system is an inevitable trend of development. It canautomatically capture surveillance video image sequence, detect, identificate andtrack moving targets in real time. By analyzing image screen, the intelligent systemcan proactively uncover violations, suspicious targets and potential hazards.And thenquickly alerting by a reasonable way, it can guide the activation of correspondinglinkage controls. The implementation of coalmine intelligent video surveillancesystem requires using a number of technologies such as image processing, machinelearning and computer vision and so on. This paper studies four key technologies; itsspecific tasks are as follows:In order to clear coal fog dust images with random noise, an algorithm of fogdust removal and simultaneously denoising based on DCPBF is proposed. This paperestablishes a coalmine fog dust image degradation model, designs methods andprocedures for estimating ambient light and rough transmittance based on darkchannel prior. The fine transmittance diagram is quickly obtained by joint bilateralfiltering. A regularization objective function is constructed based on the imagedegradation model. By solving a converted image and Gaussian bilateral filtering theimage, fog dust removal and simultaneously denoising are realized.Aiming at the relatively static background of coalmine video surveillanceenvironment, this paper uses background subtraction method for moving targetdetection. An adaptive background modeling and updating method based onclustering technology is proposed. Clustering pixel gray values by improved FCMalgorithm, a different number of classifications are adaptively selected to build thebackground model of each pixel. With scene changes classifications are updated,added and deleted thus completing background update. Image foreground is detectedby jointing background differential information, three differential information andspatial information. Differential threshold is automatically setted by improved OTSUmethod. A moving shadow detection method using pixel luminance and texture isproposed. Because the pixel brightness and texture value are invariant in gray imagesbefore and after shadow covering, moving shadow detection and removal can berealized.Considerring single-target tracking as an online classification problem between target an background, a linear SVM is used as a classification tool. An FLSVMILmethod with sample reduction mechanism is proposed to realize online updatingclassifiers. And so an single-target tracking algorithm based on FLSVMILis proposed.Due to the interference of invalid history information and the nonlinear separability ofsample set, an single-target tracking algorithm based on LSVMSE is proposed.Moving target is tracking by an ensemble classifier.According to the requirement of multi-target tracking mission in the coalmineintelligent video surveillance system, an multi-target tracking algorithm based onUKF-MHT is proposed. This paper designs the basic algorithm framework, anddetermines the treatment of critical steps which include setting tracking gate,matching target predicted value and observed value, evaluating and removing track,clustering track and generating m-best hypotheses, predicting and updating targetstates. In the process of adaptive tracking correction, specific discriminant strategiesand correction method are designed for three types of tracking error caused by targettemporary loss, target adhesions and split.In this paper, there are fifty-seven figures, twenty-one tables, and one hundredand fifty-eight reference documents.