Efficient covering of target areas using a location prediction-based algorithm
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Efficient covering of target areas using a location prediction‑based algorithm Seok‑Woo Jang1 Accepted: 29 October 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Due to the rapid development of the high-speed wired and wireless Internet, image contents including objects with exposed personal information are being distributed freely, which is a social problem. In this paper, we introduce a method of robustly detecting a target object with facial region exposed from an image that is quickly entered using skin color and a deep learning algorithm and effectively covering the detected target object through prediction. The proposed method in this paper accurately detects the target object containing facial region exposed from the image entered by applying an image adaptive skin color model and a CNN-based deep learning algorithm. Subsequently, the location prediction algorithm is used to quickly track the detected object. A mosaic is overlapped over the target object area to effectively protect the object area where the facial region is exposed. The experimental results show that the proposed approach accurately detects the target object including the facial region exposed from the continuously entered video, and efficiently covers the detected object through mosaic processing while quickly tracking it using a prediction-based tracking algorithm. The tracking-based target covering method proposed in this study is expected to be useful in various practical applications related to pattern recognition and image security, such as content-based image retrieval, real-time surveillance, human–computer interaction, and face detection. Keywords Target covering · Deep learning · Prediction algorithm · Video data · Social problem
* Seok‑Woo Jang [email protected] 1
Department of Software, Anyang University, 22, 37‑Beongil, Samdeok‑Ro, Manan‑Gu, Anyang 430‑714, Korea
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1 Introduction The supply of smart devices with high performance and the rapid development of cameras with good quality enabled the general public to easily and freely acquire video data through the high-speed wired and wireless Internet. Such image data include various kinds of video big data such as photos, CCTV videos, black box images of cars, UCC (user created contents), and movies [1–5]. In addition, the acquired image big data are useful in a variety of related practical applications such as robot vision, content-based image retrieval, real-time surveillance, human–computer interaction, face detection, and autonomous driving [6–10]. On the other hand, various types of image big data that contain objects to which personal information is exposed, such as human nudity, facial area, phone number, and home address can also be easily acquired and distributed over the Internet. Therefore, it is becoming a social problem [11, 12]. In other words, the psychological damage experienced by the persons concerned who witnessed the scene where personal information or images containing exposed par
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