An adaptive feature comparison method for real-time video identification

This paper proposes an adaptive feature comparison method for real-time video identification. The proposed method continuously analyzes the input video signal and compares them with prestored video sequences in feature domain adaptively to the analysis result. The number of features for comparison and thresholds for similarity matching are dynamically controlled to identify the input video correctly in real-time. This parameter control enables high-speed matching by minimizing redundant matching and improves robustness against analog noise. Experimental results show that it achieves identification with no errors from a large video clip database and realtime identification from more than 200,000 video clips of 15 seconds.