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. 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. 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%. 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.