本文選題：礦井視頻監(jiān)控 + 車載視頻��； 參考：《工礦自動化》2017年11期

【摘要】：針對礦井車載攝像系統(tǒng)拍攝的視頻因含有前景運動目標及高噪聲造成的全局運動矢量估計誤匹配率高、實時性較差等問題,提出了一種基于ORB特征匹配與改進粒子濾波的礦井車載視頻圖像穩(wěn)像算法。在運動矢量估計階段,采用ORB算法提取圖像特征點;采用基于圖像塊的連續(xù)3幀間差分法,聯(lián)合時空一致性準則快速剔除前景運動區(qū)域的特征點;結(jié)合前景標記區(qū)域,對特征點位置進行初次篩選,對保留下來的背景特征點進行配準;利用仿射變換模型實現(xiàn)幀間運動矢量的估計。在運動濾波階段,采用基于估計窗的實時粒子濾波算法濾除抖動分量,獲得補償參數(shù)。實驗結(jié)果表明,該算法有效避免了前景運動目標對穩(wěn)像精度的影響,且具有較快的處理速度。
[Abstract]:Aiming at the problems of high mismatch rate of global motion vector estimation caused by the existence of foreground moving target and high noise in the video recorded by the vehicle camera system in mine, the real time performance of the video is poor, and so on. An image stabilization algorithm based on Orb feature matching and improved particle filter is proposed. In motion vector estimation stage, Orb algorithm is used to extract image feature points, image block based continuous three frame difference method is used to quickly eliminate feature points of foreground moving region by using spatio-temporal consistency criterion, and foreground tagging region is combined. The location of feature points is first selected, the remaining background feature points are registered, and the motion vectors between frames are estimated by affine transformation model. In the phase of motion filtering, the real time particle filter algorithm based on estimation window is used to filter the jitter component and obtain the compensation parameters. Experimental results show that the proposed algorithm can effectively avoid the influence of foreground moving targets on image stabilization accuracy and has a faster processing speed.
【作者單位】： 中國礦業(yè)大學(xué)信息與控制工程學(xué)院;徐州市公安局科技處;
【基金】：江蘇省“六大人才高峰”高層次人才培養(yǎng)項目(2015-ZBZZ-009)
【分類號】：TD76;TP391.41

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1 牛維;頭盔攝像頭視頻的穩(wěn)像算法研究[D];西北大學(xué);2014年

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本文編號：2085898