Automatic moving object detection has gained increased research interest due to its widespread applications like security provision, traffic monitoring, and various types of anomalies detection, etc. In the video surveillance system, the video is processed for the detection of motion objects in a step-by-step process. However, the detection has become complex and less effective due to various complex constraints. To obtain an effective performance in the detection of motion objects, this research work focuses to develop an automatic motion object detection system based on the statistical properties of video and supervised learning. In this paper, a novel Background Modeling mechanism is proposed with the help of a Biased Illumination Field Fuzzy C-means algorithm to detect the moving objects more accurately. Here, the non-stationary pixels are separated from stationary pixels through the Background Subtraction. Afterward, the Biased Illumination Field Fuzzy C-means approach has accomplished to improve the segmentation accuracy through clustering under noise and varying illumination conditions. The performance of the proposed algorithm compared with conventional methods in terms of accuracy, precision, recall, and F- measure.

中文翻译：

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在视频监控系统中使用自适应背景建模机制进行有效的运动目标检测

自动运动物体检测由于其广泛的应用，如安全提供、交通监控和各种类型的异常检测等而引起了越来越多的研究兴趣。在视频监控系统中，对视频进行处理以检测运动物体的步骤：一步一步的过程。然而，由于各种复杂的约束，检测变得复杂且效率较低。为了在运动物体检测中获得有效的性能，本研究工作的重点是开发一种基于视频统计特性和监督学习的自动运动物体检测系统。在本文中，在偏置照明场模糊C-means算法的帮助下，提出了一种新的背景建模机制，以更准确地检测运动物体。这里，非静止像素通过背景减法与静止像素分离。之后，Biased Illumination Field Fuzzy C-means 方法通过在噪声和变化的光照条件下进行聚类来提高分割精度。与传统方法在准确率、准确率、召回率和 F 度量方面的性能相比，所提出的算法的性能。