Internet services that share vehicle black box videos need a way to obfuscate license plates in uploaded videos because of privacy\nissues.Thus, plate detection is one of the critical functions that such services rely on. Even though various types of detection methods\nare available, they are not suitable for black box videos because no assumption about size, number of plates, and lighting conditions\ncan bemade.We propose amethod to detect Korean vehicle plates fromblack box videos. It works in two stages: the first stage aims\nto locate a set of candidate plate regions and the second stage identifies only actual plates fromcandidates by using a support vector\nmachine classifier. The first stage consists of five sequential substeps. At first, it produces candidate regions by combining single\ncharacter areas and then eliminates candidate regions that fail to meet plate conditions through the remaining substeps. For the\nsecond stage, we propose a feature vector that captures the characteristics of plates in texture and color. For performance evaluation,\nwe compiled our dataset which contains 2,627 positive and negative images.The evaluation results show that the proposed method\nimproves accuracy and sensitivity by at least 5% and is 30 times faster compared with an existing method.
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