Video Structural Description: A Semantic Based Model for Representing and Organizing Video Surveillance Big Data

Big data is an emerging paradigm applied to datasets whose size is beyond the ability of commonly used software tools to capture, manage, and process the data within a tolerable elapsed time. Especially, the data volume of all video surveillance devices in Shanghai, China is up to 1 TB every day. Thus, it is important to accurately describe the video content and enable the organizing and searching potential videos in order to detect and analyze related traffic events. Unfortunately, raw data and low level features cannot meet the video based tasks. In this paper, we propose a semantic based model for representing and organizing video big data. The proposed method defines a number of concepts and their relations, which allow users to use them to annotate video traffic events. The defined concepts including people, vehicle, and traffic sigh, which can be used by users for annotating and representing video traffic events unambiguous. In addition, we define the spatial and temporal relations in event and concepts definitions, which can be used by users for annotating and representing the semantic relations between objects in video traffic events. Moreover, semantic link network is used for organizing video resources based on their associations. In the application, we illustrate two systems using the proposed method for annotating and searching video resources.