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Moving Object Detection for Dynamic Background Scenes Based on Spatiotemporal Model 时间:2017-09-14 15:11   来源:未知   作者:admin   点击: 次    Abstract:Moving object detection in video streams is the first step of many computer vision applications. Background modeling and subtraction for moving detection is the most common technique for detecting, while how to detect moving objects correctly is still a challenge. Some methods initialize the background model at each pixel in the first N frames. However, it cannot perform well in dynamic background scenes since the background model only contains temporal features. Herein, a novel pixelwise and nonparametric moving object detection method is proposed, which contains both spatial and temporal features. The proposed method can accurately detect the dynamic background. Additionally, several new mechanisms are also proposed to maintain and update the background model. The experimental results based on image sequences in public datasets show that the proposed method provides the robustness and effectiveness in dynamic background scenes compared with the existing methods.
1. Introduction
  Recently, background modeling and subtraction became the most popular technique for moving object detection in computer vision, such as object recognition and traffic surveillance [1–9].
  Compared to optical flow [10, 11] and interframe difference algorithms [12], background subtraction algorithm needs less computation and performs better, and it is more flexible and effective. The idea of background subtraction is to differentiate the current image from a reference background model. These algorithms initialize a background model at first to represent the scene with no moving objects and then detect the moving objects by computing the difference between the current frame and the background model. Dynamic background is a challenge for background subtraction, such as waving tree leaves and ripples on river. In the past several years, many background subtraction algorithms have been proposed, and most of them focus on building more effective background model to handle dynamic background as follows:
(1)Features: texture and color [13–15]
(2)Combining methods: combining two or more background models as the new model [16]
(3)Updating the background model [17]
In this paper, a new pixelwise and nonparametric moving object detection method is proposed. Background model is built by the first frames and sampling times in 3 × 3 neighborhood region randomly. On the one hand, spatiotemporal model represents dynamic background scenes well. On the other hand, a new update strategy makes the background model fit the dynamic background. In addition, the proposed method can deal with ghost well. Experimental results show that the proposed method can efficiently and correctly detect the moving objects from the dynamic background.
  This paper is organized as follows. In the next section, an overview of existing approaches of background subtraction is presented. Section 3 describes the proposed method in detail, and then Section 4 provides the experimental results and comparison with other methods. Section 5 includes conclusions and further research directions.
2. Related Work
   In this section, some background subtraction methods will be introduced, which are divided into parametric and nonparametric models.
For parametric models, the most commonly used method is Gaussian Mixture Model (GMM) [18]. Before GMM, a per-pixel Gaussian model was proposed [19], which calculated the mean and standard deviation for each pixel at first and then compared the probability with a certain threshold of each pixel to classify the current pixel as background or foreground. But this Gaussian model cannot deal with noise and dynamic situation. GMM was proposed to solve these problems. GMM usually set three-to-five Gaussian models for each pixel and updated the model after matching. Several papers [20, 21] improved the GMM method to be more flexible and efficient in recent years.