A framework for fingerprint-based detection of repeating objects in multimedia streams

We present an original framework for the detection of repeating objects in multimedia streams. This framework is designed so that it can work with any fingerprint model. A fingerprint is extracted for each incoming frame of the multimedia stream. The framework then manages this fingerprint so that if one similar frame comes later in the stream, it will be identified as a repetition. The framework has been tested with two distinct fingerprint models on simulated and `real-world' data. The results show that the framework performs well with both presented models and that it is suitable for industrial use-cases.