With the rapid development of intelligent transportation, the video surveillance system as its important component has been achieved much attention. Traffic condition closely related to people’s lives needs to be tracked in time. Some methods estimate traffic flow by analyzing the pictures taken by fixed cameras. However, they can only estimate the traffic condition of particular roads. Different from the traditional traffic flow estimation methods, the proposed method explores the video information rather than traffic images acquired by sensing remote-sensing sensors in this letter. More specifically, the highlights of our work include the following parts: first, change detection is performed on analyzing the difference between one frame image extracted from Unmanned Aerial Vehicle (UAV) videos and an updated background image for the sake of recognizing the whole profile of every moving object. Second, a modified fuzzy c-means method is engaged in the process of change detection, which segments the road regions to enhance the profiles of moving objects and eliminate the noise of complex backgrounds. Finally, the estimation of traffic flow can be achieved by analyzing the change detection result. Besides, the videos shot by UAV on a crossroad are used to analyze the effectiveness of the proposed method. Experimental results on a series of binary images and proportion illustrations demonstrate the promising performance of the proposed method in terms of human visual perception and segmentation accuracy.

随着智能交通的迅猛发展，视频监控系统作为其重要的组成部分已经引起了广泛的关注。与人们生活息息相关的交通状况需要及时跟踪。一些方法通过分析固定摄像机拍摄的照片来估算交通流量。但是，他们只能估算特定道路的交通状况。与传统的交通流量估计方法不同，本文提出的方法探索视频信息，而不是通过感测遥感传感器获取的交通图像。更具体地说，我们工作的重点包括以下几部分：首先，为了识别每个运动物体的整体轮廓，对从无人机（UAV）视频中提取的一帧图像与更新的背景图像之间的差异进行分析以进行变化检测。其次，将改进的模糊c均值方法应用于变化检测过程中，该方法对道路区域进行分割以增强运动对象的轮廓并消除复杂背景的噪声。最后，可以通过分析变化检测结果来实现交通流量的估计。此外，还利用无人机在十字路口拍摄的视频来分析该方法的有效性。在一系列二值图像和比例插图上的实验结果证明了该方法在人类视觉感知和分割精度方面的有希望的性能。