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http://dx.doi.org/10.9711/KTAJ.2022.24.3.247

A study for improvement of far-distance performance of a tunnel accident detection system by using an inverse perspective transformation  

Lee, Kyu Beom (Dept. of Future & Smart Construction Research, Korea Institute of Civil Engineering and Building Technology, Smart City and Construction Convergence, University of Science & Technology)
Shin, Hyu-Soung (Dept. of Future & Smart Construction Research, Korea Institute of Civil Engineering and Building Technology)
Publication Information
Journal of Korean Tunnelling and Underground Space Association / v.24, no.3, 2022 , pp. 247-262 More about this Journal
Abstract
In domestic tunnels, it is mandatory to install CCTVs in tunnels longer than 200 m which are also recommended by installation of a CCTV-based automatic accident detection system. In general, the CCTVs in the tunnel are installed at a low height as well as near by the moving vehicles due to the spatial limitation of tunnel structure, so a severe perspective effect takes place in the distance of installed CCTV and moving vehicles. Because of this effect, conventional CCTV-based accident detection systems in tunnel are known in general to be very hard to achieve the performance in detection of unexpected accidents such as stop or reversely moving vehicles, person on the road and fires, especially far from 100 m. Therefore, in this study, the region of interest is set up and a new concept of inverse perspective transformation technique is introduced. Since moving vehicles in the transformed image is enlarged proportionally to the distance from CCTV, it is possible to achieve consistency in object detection and identification of actual speed of moving vehicles in distance. To show this aspect, two datasets in the same conditions are composed with the original and the transformed images of CCTV in tunnel, respectively. A comparison of variation of appearance speed and size of moving vehicles in distance are made. Then, the performances of the object detection in distance are compared with respect to the both trained deep-learning models. As a result, the model case with the transformed images are able to achieve consistent performance in object and accident detections in distance even by 200 m.
Keywords
Tunnel CCTV-based accident detection system; Inverse perspective transform; Perspective; Transformed image; Deep learning;
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