Research and application of security video labeling platform With the rapid development of science and technology, the intelligent security technology has developed rapidly. As far as the deep learning method used in the field of intelligent security is concerned, image marking is a heavy and complicated work. The current security video is usually manually completed, which increases the operation and maintenance cost of security video system. For this reason, this paper proposes a semiautomatic annotation method based on neighborhood rough set for the complexity of distorted image annotation. The division of theoretical domain and the definition of correlation degree are redone. The aim is to start with the manual annotation of the data source, improve the quality of the annotation, and solve the difficulty of the annotation caused by the standard deviation of the subjective evaluation of the annotator and the blurring of the boundary between the annotated words. The application example shows that the security video labeling platform can realize semiautomatic video labeling and significantly improve the accuracy rate of security video labeling.

A neighborhood rough set model with nominal metric embedding Abstract   Rough set theory is an essential tool for measuring uncertainty, which has been widely applied in attribute reduction algorithms. Most of the related researches focus on how to update the lower and the upper approximation operator to match data characteristics or how to improve the efficiency of the attribute reduction algorithm. However, in the nominal data environment, existing rough set models that use the Hamming metric and its variants to evaluate the relations between nominal objects can not capture the inherent ordered relationships and statistic information from nominal values due to the complexity of data. The missing information will affect the accuracy and validity of the data representation, thereby reducing the reliability of rough set models. To overcome this challenge, we propose a novel object dissimilarity measure, i.e., relative object dissimilarity metric(RODM) that learned from nominal data to replace the Hamming metric and then construct a xcfx88neighborhood rough set model. It extends the classical rough set model to a robust, representative, and effective model which is close to the characteristics of nominal data. Based on the xcfx88neighborhood rough set model, we propose a heuristic twostage attribute reduction algorithm(HTSAR) to perform the feature selection task. Experiments show that the xcfx88neighborhood rough set model can take advantage of more potential knowledge in nominal data and achieve better performance for attribute reduction than the existing rough set model.