Fast Background Initialization with Recursive Hadamard Transform

In this paper, we present a new and fast technique for background estimation from cluttered image sequences. Most of the background initialization approaches developed so far collect a number of initial frames and then require a slow estimation step which introduces a delay whenever it is applied. Conversely, the proposed technique redistributes the computational load among all the frames by means of a patch by patch preprocessing, which makes the overall algorithm more suitable for real-time applications. For each patch location a prototype set is created and maintained. The background is then iteratively estimated by choosing from each set the most appropriate candidate patch, which should verify a sort of frequency coherence with its neighbors. To this aim, the Hadamard transform has been adopted which requires less computation time than the commonly used DCT Finally, a refinement step exploits spatial continuity constraints along the patch borders to prevent erroneous patch selections. The approach has been compared with the state of the art on videos from available datasets (ViSOR and CAVIAR), showing a speed up of about 10 times and an improved accuracy.