Research On Object Detection And Tracking In Underground Environment

Detecting and tracking moving object is the basis of behavioral analysis ofmoving targets, and has always been an active research topic in the field of computervision. The video surveillance system currently plays an important role in the safetyproduction of coal mine. Against the problems in the video surveillance system underthe complex environment of coal mine, this dissertation studied and discussed the keyproblems in the detecting and tracking of the moving object in the coal mine bysummarizing the existing research results with its main contents being as follows:1. Against the characteristic that target and background change dynamically, thisdissertation proposed a moving object detection method based on robustness FuzzyKernel-clustering. Then we could overcome the disadvantages of high computingcomplexity and high memory usage rate of the MOG method.2. Against the disadvantages of sensitive starting point and unstable clusteringresults caused by K-mean algorithm based on the BoW model, this dissertationproposed the EMMC method. With the EMMC method the initial clustering obtainedthrough the K-mean in the BoW model was optimized to effectively improve theaccuracy of the visual dictionary.3. For the particle degradation in the particle filter algorithm in the process oftracking the moving object, this dissertation proposes the ELM method to improve theparticle filter tracking. Meanwhile, we used the global color feature and local DAISYfeature fusion to build the target representation model and improve the robustness ofthe target tracking algorithm.4. Against the problem that particle filter cannot be applied to the long-termtracking occasions, this dissertation proposed a long-term particle filter trackingmethod that combines the co-training classifiers. The non-overlapping classifier gridwas applied to solve the problem of restarting the particle filter tracking after trackingfailure.