【摘要】：ViBe算法存在鬼影和靜止目標(biāo)問題,這些問題給目標(biāo)檢測(cè)帶來誤差,需要快速有效地抑制。文章在原始ViBe算法基礎(chǔ)上,先通過比較局部區(qū)域的背景模型像素值方差和新來幀該區(qū)域的像素值方差的大小來判斷該區(qū)域存在鬼影還是靜止目標(biāo),存在則進(jìn)行抑制,然后采用不同的策略更新鬼影區(qū)域和靜止目標(biāo)區(qū)域的背景。實(shí)驗(yàn)結(jié)果中,改進(jìn)算法僅用15幀就可以完全抑制鬼影,僅用20幀就能完全抑制靜止目標(biāo),而原始ViBe算法完全抑制鬼影需要108幀且抑制靜止目標(biāo)能力有限。實(shí)驗(yàn)結(jié)果表明,對(duì)于普通的以及背景較為復(fù)雜的監(jiān)控場(chǎng)景,文中改進(jìn)算法可行、有效。
[Abstract]:Vibe's algorithm has the problem of ghost and stationary object, which can bring the error to the target detection, and it needs to be effectively suppressed. On the basis of the original Vibe algorithm, the existence of ghost or stationary object in the region is judged by comparing the pixel value variance of the background model of the local region and the magnitude of the variance of the pixel value of the new frame. The background of the ghost region and the stationary target region is then updated with a different strategy. In the experimental results, the improved algorithm can completely suppress the ghost image by only 15 frames, can completely suppress the static target with only 20 frames, and the original Vibe algorithm completely suppresses the ghost image to need 108 frames and has limited static target capability. The experimental results show that the improved algorithm is feasible and effective for common and complex monitoring scenarios.
【作者單位】： 合肥工業(yè)大學(xué)計(jì)算機(jī)與信息學(xué)院;合肥工業(yè)大學(xué)電子科學(xué)與應(yīng)用物理學(xué)院;
【基金】：國家自然科學(xué)基金資助項(xiàng)目(61371155) 安徽省科技攻關(guān)計(jì)劃資助項(xiàng)目(1301b042023)
【分類號(hào)】：TP391.41;TN948.6

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