Metadata Extraction Using DeepLab V3 and Probabilistic Latent Semantic Analysis for Intelligent Visual Surveillance Systems

Recently, surveillance cameras are ubiquitous for both real-time monitoring and recording important moments. Temporarily seamless surveillance using multiple cameras requires increasing amount of human efforts and enormous size of storage. The use of dynamic cameras further requires advanced computer vision algorithms, and is another challenge for intelligent visual surveillance. To solve those problems, we present an enhanced metadata extraction method for robust object search and a person re-identification. More specifically, the proposed method accurately extracts an object region using a modified DeepLab version 3, and then extracts metadata including representative color, size, aspect ratio, and moving trajectory of the object. The proposed metadata extraction method can be applied to a wide range of surveillance systems such as search for missing children in a large public space and crowd monitoring system.