This paper deals with a method for removing a ghost target that is not a real object from the output of a multiple object-tracking algorithm. This method uses an artificial neural network (multilayer perceptron) and introduces a structure, learning, verification, and evaluation method for the artificial neural network. The implemented system was tested at an intersection in a city center. Results from a 28-min measurement were 88% accurate when the multilayer perceptron for ghost target classification successfully detected the ghost targets, and 6.7% inaccurate when ghost targets were mistaken for actual targets. This method is expected to contribute to the advancement of intelligent transportation systems if the weaknesses revealed during the evaluation of the system are complemented and refined.

In this paper, we suggest the development of object detection systems for the safety of the ship through the study of the properties of short-range radar. Many of the short-range radars developed for special purpose like cars has cheaper price advantages but it is not proper to every application. In order to overcome such obstacles we need to analysis data from experiments in various environments and feature analysis of the device is essential. Also, the data clustering algorithms to display correct classified moving objects is necessary. In this paper we propose the advanced fast moving object detection system using short range radars with better detection accuracy. And we proposed a clustering algorithm using the value of the RCS and the speed and trajectory information of the radar data that are reflected.

Determination of type of a vehicle is being used in various areas such as collecting tolls, collecting statistical traffic data and traffic prognosis. Because most of the vehicle type classification systems depend on vehicle length indirectly or directly, highly reliable automatic vehicle length measurement system is crucial for them. This study makes use of a pencil beam laser rangemeter and devises a mechanical device which rotates the laser rangemeter. The implemented system measures the range between a point and the laser rangemeter then indicates it as a spherical coordinate. We obtain several silhouettes of cross section of the vehicle, the rate of change of the silhouettes, signs of the rates then squares the rates to apply cell averaging constant false alarm rate (CA-CFAR) technique to find out where the border is between the vehicle and the background. Using the border and trigonometry, we calculated the length of the vehicle and confirmed that the calculated vehicle length is about 94% of actual length.

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