Threshold learning from samples drawn from the null hypothesis for the generalized likelihood ratio cusum test

Abstract

Although optimality of sequential tests for the detection of a change in the parameter of a model has been widely discussed, the test parameter tuning is still an issue. In this communication, we propose a learning strategy to set the threshold of the GLR CUSUM statistics to take a decision with a desired false alarm probability. Only data before the change point are required to perform the learning process. Extensive simulations are performed to assess the validity of the proposed method. The paper is concluded by opening the path to a new approach to multi-modal feature based event detection for video parsing