Efficient covering of target areas using a location prediction-based algorithm

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.

Prediction Algorithm of Collaborative Innovation Capability of High-End Equipment Manufacturing Enterprises Based on Random Forest

“…where Gini represents the Gini coefficient, T represents the number of feature categories, and P t represents the probability that the food indicator is the current feature. Equation (1) shows that the Gini coefficient is the probability of inconsistency between two samples taken from the data and the category sign of the samples. e smaller the value of Gini coefficient, the higher the classification purity of the model, and therefore the better the classification accuracy of the data [12].…”

“…As for the definition of the scope of highend equipment manufacturing industry, the Ministry of Industry and Information Technology of China divided eight areas during the 13th Five Year Plan Period: aerospace equipment, marine engineering equipment and high-tech ships, advanced rail transit equipment, high-end CNC machine tools, robot equipment, modern agricultural machinery equipment, high-performance medical machinery, and advanced chemical complete sets of equipment. ese eight industries come down in one continuous line with the manufacturing power strategy proposed by [1].…”