• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.4
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• pp.32-40
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• 2008

Since vision-based surveillance system uses a conventional camera which has a narrow field of view, it is difficult to apply it into the environment whose the ceiling is low and the monitoring area is wide. To overcome this problem, the method of increasing the number of camera causes the increase of the cost and the difficulties of camera set-up For these problems, we propose a new surveillance system based on multiple fisheye cameras which have 180 degree field of view. The proposed method handles occlusions using the homography relation between the multiple fisheye cameras. In the experiment, four fisheye cameras were set up within the area of $17{\times}14m$ at height of 2.5 m and five people wandered and crossed with one another within this area. The detection rates of the proposed system was 83.0% while that of a single camera was 46.1%.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.7
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• pp.634-640
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• 2015

This paper proposes a novel three-dimensional mapping algorithm in Omni-Directional Vision SLAM based on a fisheye image and laser scanner data. The performance of SLAM has been improved by various estimation methods, sensors with multiple functions, or sensor fusion. Conventional 3D SLAM approaches which mainly employed RGB-D cameras to obtain depth information are not suitable for mobile robot applications because RGB-D camera system with multiple cameras have a greater size and slow processing time for the calculation of the depth information for omni-directional images. In this paper, we used a fisheye camera installed facing downwards and a two-dimensional laser scanner separate from the camera at a constant distance. We calculated fusion points from the plane coordinates of obstacles obtained by the information of the two-dimensional laser scanner and the outline of obstacles obtained by the omni-directional image sensor that can acquire surround view at the same time. The effectiveness of the proposed method is confirmed through comparison between maps obtained using the proposed algorithm and real maps.

• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.418-427
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• 2016

A smart parking management requires to track a vehicle in a indoor parking lot and to detect the place where the vehicle is parked. An advanced parking system watches all space of the parking lot with CCTV cameras. We can use these cameras for vehicles tracking and detection. In order to cover a wide area with a camera, a fisheye lens is used. In this case the shape and size of an moving vehicle vary much with distance and angle to the camera. This makes vehicle detection and tracking difficult. In addition to the fisheye lens, the vehicle headlights also makes vehicle detection and tracking difficult. This paper describes a method of realtime vehicle detection and tracking robust to the harsh situation described above. In each image frame, we update the region of a vehicle and estimate the vehicle movement. First we approximate the shape of a car with a quadrangle and estimate the four sides of the car using multiple histograms of oriented gradient. Second we create a template by applying a distance transform to the car region and estimate the motion of the car with a template matching method.