A study on video semantics; overview, challenges, and applications

Due to the increase in surveillance systems, there is a massive increase in surveillance data. As of now, the key challenge for video surveillance systems is analyzing these large video clips. It, therefore, has an enormous demand for intelligent video analysis systems capable of identifying activities and events. Since many researchers have emphasized the role of contextual knowledge and how the performance of video content analysis has improved in several ways, we have looked at different approaches in this study that can extract semantic information to human-level perception in the video. We also addressed open problems in semantics that come from event detection and irregular activity detection. Most methods/models are too coarse to accurately extract a complete set of information. Thus, we need to use a machine-readable format to view, process, store and extract meaningful information from the video data. In this paper, we discussed the methods/approaches for extracting low-level features, mid-level features, and high-level video features and their representation using Semantic Technologies. A taxonomy of hierarchical feature generation approaches is also provided. Some evaluation metrics for evaluating video activity and measuring the performance of the extraction features are explored. Community-approved benchmark datasets are also thoroughly surveyed and presented. The paper provides a complete framework of video research to develop an intelligent surveillance system.