Fast semantic region analysis for surveillance & video databases
Video databases are broadly applied both in consumer and professional domains. The importance of real-time surveillance video monitoring has increased for security reasons, while also for consumers video databases are rapidly growing. Quickly searching in databases is facilitated by region analysis, as it provides an indication for the contents of the video. For real-time and cost-efficient implementations, it is important to develop algorithms with low computational complexity. In this paper, we analyze the complexity of a newly develop semantic region labeling approach [2], e.g. road, sky, etc., which aims at extracting spatial contextual information from a video. In the analyzed semantic region labeling approach, color and texture features are combined with the vertical position to label the key regions. The algorithm is analyzed by its native DSP computations and memory usage to prove its practical feasibility. The analysis results show that the system has a low complexity while offering high-accuracy region labeling. A comparison with the state-of-the-art algorithm convincingly reveals that our system outperforms the state-of-the-art with fewer computations.