Efficient Algorithm for Initialization of Object Tracks Based on Changepoint Detection Method

In many problems of the initialization of objects' tracks, changepoint detection algorithms can be used. In the past when computational complexity was an issue, the K / N algorithm gained its popularity due to computational simplicity. Nowadays with the tremendous progress in computing technology, the problem of finding more efficient detection and track initiation algorithms is urgent. A substantially more efficient track initiation algorithm can be built based on the sequential change detection technique. In this paper, we consider the Finite Moving Average algorithm. We compare the performance of the K / N algorithm and the Finite Moving Average algorithm. The optimality criterion is to maximize the probability of correct detection in a certain time interval under the given false alarm risk measured as the local probability of a false alarm. For performance, we obtain a theoretical estimate and an estimate by Monte Carlo (MC) simulations. The results show that the Finite Moving Average algorithm performs significantly better than the K / N procedure.