Abstract :
We describe a system based on exact-duplicate matching for detecting and localizing TV commercials in a video stream, clustering the exact duplicates, and detecting duplicate exact-duplicate clusters across video streams. A two-stage temporal recurrence hashing algorithm is used for the detection, localization, and clustering. The algorithm is fully unsupervised, generic, and ultrahigh speed. Another algorithm is used to integrate the video and audio streams to achieve higher performance extraction. Its sequence- and frame-level accuracies in testing were respectively 98.1% and 97.4%. A third algorithm uses a new bag-of-fingerprints model to detect duplicate exact-duplicate clusters across multiple streams. It is robust against decoding errors. Its contributions include: 1) fully unsupervised detection, extraction, and matching of exact duplicates; 2) more generic commercial detection than with the knowledge-based techniques; 3) ultrahigh-speed processing, which detected the TV commercials from a one-month video stream in less than 42 minutes, which is more than ten times faster than with state-of-the-art algorithms; and 4) more generic operation in terms of signal input, the performance of which is consistent between video and audio streams. Testing using a video database containing a ten-hour, a one-month, and a five-year video stream comprehensively demonstrates the effectiveness and efficiency of this system.
