动态背景下自适应LOBSTER算法的前景检测

Foreground detection of the adaptive LOBSTER algorithm in a dynamic background

目的前景检测是视频监控领域的研究重点之一。LOBSTER(local binary similarity segmenter)算法把Vi Be(visual background extractor)算法和LBSP(local binary similarity patterns)特征结合起来,在一般场景下取的了优良的检测性能,但是LOBSTER算法在动态背景下适应性差、检测噪声多。针对上述问题,提出一种改进的LOBSTER算法。方法在模型初始化阶段,计算各像素的LBSP特征值,并分别把像素的灰度值和LBSP特征值添加到各像素的颜色背景模型与LBSP背景模型中,增强了背景模型的描述能力;在像素分类阶段,根据背景复杂度自适应调整每个像素在颜色背景模型和LBSP背景模型中的分类阈值,降低了前景中的噪声;在模型更新阶段,根据背景复杂度自适应调整每个像素背景模型的更新策略,提高背景模型对动态背景的适应能力。结果本文算法与Vi Be算法和LOBSTER算法进行了对比实验,本文算法的前景图像比Vi Be算法和LOBSTER算法的噪声点大幅较低,本文算法的PCC指标在不同视频库中比Vi Be算法提高0.736%7.56%,比LOBSTER算法提高0.77%12.47%,FPR指标不到Vi Be算法和LOBSTER算法的1%。结论实验仿真结果表明,在动态背景的场景下,本文算法比Vi Be算法和LOBSTER算法检测到的噪声少,具有较高的准确率和鲁棒性。

Objective Foreground detection is a key research area in the field of video surveillance. The local binary similarity segmenter （LOBSTER） algorithm combines the visual background extractor （ViBe） algorithm with the local binary similarity patterns （LBSP） feature, which obtains excellent detection performance in general scenes. However, it has poor adaptability and high detection noise in the dynamic background. An improved LOBSTER algorithm is proposed to solve the aforementioned problems. Method The LBSP value of each pixel is calculated at the initialization stage of the model. The gray and LBSP values of the pixel are then added to each pixel of the color background model and LBSP background models, respectively, which enhances the description of the background model. The standard deviation, which is calculated in the neighborhood of each pixel, is utilized as a measurable index of the complexity of the pixel at the pixel classification stage. Adaptively adjusting the classification threshold in the color and LBSP background models can lower the noise in the foreground according to the background complexity. The conservative update strategy is still used in the improved LOBSTER algorithm to update the LOBSTER background modal at the model updating stage. When a pixel is determined as the background, the pixel update is adopted as its own background model. If the background complexity of the pixel is smaller than a certain threshold, then the pixel is also added to the background model of the neighborhood by the probability of 1/φ, wherein the general value of φ is 16. If the background complexity is larger than a certain threshold, a new pixel is randomly selected in the pixel neighborhood which is classified as a background pixel. The selected pixel is then added to its own background model by the probability of 1/φ. The adaptability in the dynamic background is improved by adaptively updating the model strategy. Result Many qualitative analysis and quantitative calculations are presented in the ChangeDetection database for the improved LOBSTER algorithm in this study. The noise in the foreground image of the improved algorithm is less than that of the ViBe and LOBSTER algorithms. The value of the improved algorithm is higher by 0. 736% to 7.56% than the ViBe algorithm and higher by approximately 0. 77% to 12.47% than the LOBSTER algorithm in terms of the PCC index. The value of the improved LOBSTER algorithm is less than 1% of the ViBe and LOBSTER algorithms in terms of the FPC index. Conclusion Simulation results show that the improved LOBSTER algorithm performs better than the conventional ViBe model and LOBSTER algorithm in dynamic conditions. Thus, our method has a higher accurate rate and stronger robustness in foreground detection.