利用七宫格的遮挡车辆凹性检测与分割

目的:交通场景中车辆间的距离过近或相互遮挡容易造成识别上的粘连，增加了准确检测目标车辆的难度，因此需要建立有效、可靠的遮挡车辆分割机制。方法:首先在图像分块的基础上确定出车辆区域，根据车辆区域的长宽比和占空比进行多车判断；然后提出了一种基于“七宫格”的凹陷区域检测算法，用以找出车辆间的凹陷区域，通过匹配对应的凹陷区域得到遮挡区域；最后，将遮挡区域内检测出的车辆边缘轮廓作为分割曲线，从而分割遮挡车辆。结果:实验结果表明，在满足实时性的前提下算法具有较高的识别率，且能够按车辆的边缘轮廓准确分割多个相互遮挡的车辆。与其他算法相比，该算法提高了分割成功率和分割精度，查全率和查准率均可达到90%。结论:提出了一种新的遮挡车辆分割方法，有效解决了遮挡车辆不宜分割和分割不准确的问题，具有较强的适应性。

Detection and segmentation of occluded vehicles based on seven grids

Objective Intelligent transportation is a service system based on the modern electronic information technology for transportation. With the development of intelligent traffic system, video surveillance technology is often used in this area. While using the video surveillance system to detect the traffic scene, the false detections often occur when two or more vehicles approaches each other. This phenomenon increases the difficulty of vehicle detection. To overcome this problem, it is necessary to establish a reliable, practical splitting mechanism for the occluded vehicles. Further more, we proposed a new identification and segmentation algorithm for occluded vehicles. Method In order to obtain the moving areas, we use the background differencing to detect the current image. Meanwhile, t shadow areas need to eliminated. We use a shadow detection algorithm that is based on the HSV space features. Then, the image is divided into blocks to reduce the processing time. We use the width/height ratio and occupancy ratio to judge whether a moving area contains one or more vehicles. To find out the recessed area between vehicles, a new algorithm called the "seven grids" is presented. The new "seven grids" algorithm is a matrix of seven rows and seven columns. First, the recessed area detection algorithm calculates the edge of the vehicle regions. Then it uses the "seven grids" to traverse all the edges, puts the detected point at the center of the "seven grids" and determines for each point whether it is a concave point. At last, it determines the recessed areas based on the concave points, and finds the occluded areas between vehicles by matching the corresponding recessed areas. At the same time, there are some differences about the color and brightness between different vehicles. This situation is more obvious when the occlusion phenomenon exists between vehicles. Therefore, we use the algorithm for edge detection to detect these occluded areas. To identify edges of vehicles which are regarded as segmentation curves. Then we use them to segment the occluded vehicles. Result Traditional segmentation methods often work to find the segmentation points and connect the corresponding segmentation points to segment occluded vehicles. This method can effectively segment them, but the segmentation results are not accurate. The segmentation method we propose is committed to find the occluded area, and uses the edges of vehicles into the occluded area to segment occluded vehicles. The algorithm satisfies the real-time requirement and can effectively segment the occluded vehicles. Compared with other methods, it has better segmentation results and higher recognition success rate, and the recall and precision can reach up to 90%. Conclusion In this paper we focus on the effectiv identification and segmentation of occluded vehicles. We propose a segmentation method based on the "seven grids". The method segments occluded vehicles effectively, and it has strong adaptability, because it does not need any prior knowledge. Experimental results demonstrate that, this occluded vehicles detection algorithm has a high recognition rate. The proposed vehicles segmentation method can segment the overlapped ones accurately and completely.