【摘要】：目的前景檢測是視頻監(jiān)控領(lǐng)域的研究重點之一。LOBSTER(local binary similarity segmenter)算法把Vi Be(visual background extractor)算法和LBSP(local binary similarity patterns)特征結(jié)合起來,在一般場景下取的了優(yōu)良的檢測性能,但是LOBSTER算法在動態(tài)背景下適應(yīng)性差、檢測噪聲多。針對上述問題,提出一種改進的LOBSTER算法。方法在模型初始化階段,計算各像素的LBSP特征值,并分別把像素的灰度值和LBSP特征值添加到各像素的顏色背景模型與LBSP背景模型中,增強了背景模型的描述能力;在像素分類階段,根據(jù)背景復(fù)雜度自適應(yīng)調(diào)整每個像素在顏色背景模型和LBSP背景模型中的分類閾值,降低了前景中的噪聲;在模型更新階段,根據(jù)背景復(fù)雜度自適應(yīng)調(diào)整每個像素背景模型的更新策略,提高背景模型對動態(tài)背景的適應(yīng)能力。結(jié)果本文算法與Vi Be算法和LOBSTER算法進行了對比實驗,本文算法的前景圖像比Vi Be算法和LOBSTER算法的噪聲點大幅較低,本文算法的PCC指標(biāo)在不同視頻庫中比Vi Be算法提高0.736%7.56%,比LOBSTER算法提高0.77%12.47%,FPR指標(biāo)不到Vi Be算法和LOBSTER算法的1%。結(jié)論實驗仿真結(jié)果表明,在動態(tài)背景的場景下,本文算法比Vi Be算法和LOBSTER算法檢測到的噪聲少,具有較高的準(zhǔn)確率和魯棒性。
[Abstract]:Objective foreground detection is one of the key points in the field of video surveillance. The LOBSTER (local binary similarity segmenter) algorithm combines the Vi Be (visual background extractor) algorithm with the LBSP (local binary similarity patterns) feature, and it has excellent detection performance in the general scene. However, the LOBSTER algorithm has poor adaptability in dynamic background and more detection noise. To solve the above problems, an improved LOBSTER algorithm is proposed. Methods in the phase of model initialization, the LBSP eigenvalues of each pixel were calculated, and the gray value and LBSP eigenvalue of each pixel were added to the color background model and LBSP background model of each pixel respectively, which enhanced the description ability of the background model. In the pixel classification phase, the threshold of each pixel in color background model and LBSP background model is adjusted adaptively according to the background complexity to reduce the noise in the foreground. The updating strategy of each pixel background model is adaptively adjusted according to the background complexity to improve the adaptability of the background model to the dynamic background. Results compared with Vi Be algorithm and LOBSTER algorithm, the foreground image of this algorithm is much lower than that of Vi Be algorithm and LOBSTER algorithm. In this paper, the PCC index of this algorithm is 0.7367.56 higher than that of Vi Be algorithm in different video libraries, and 0.77% higher than that of LOBSTER algorithm. The PCC index of this algorithm is less than that of Vi Be algorithm and LOBSTER algorithm. Conclusion the simulation results show that the proposed algorithm can detect less noise than Vi Be algorithm and LOBSTER algorithm in dynamic background, and has higher accuracy and robustness.
【作者單位】： 江南大學(xué)物聯(lián)網(wǎng)工程學(xué)院;
【分類號】：TP391.41;TN948.6

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本文編號：2208597