An exploration of the performances achievable by combining unsupervised background subtraction algorithms
This work addresses the problem of improving motion detection in video for computer vision applications, but it is incremental as it builds on existing combination strategies.
The paper explores combining 26 unsupervised background subtraction algorithms using six strategies on the CDnet 2014 dataset, finding that combinations significantly outperform individual algorithms and compare with state-of-the-art methods like IUTIS-5 and CNN-SFC.
Background subtraction (BGS) is a common choice for performing motion detection in video. Hundreds of BGS algorithms are released every year, but combining them to detect motion remains largely unexplored. We found that combination strategies allow to capitalize on this massive amount of available BGS algorithms, and offer significant space for performance improvement. In this paper, we explore sets of performances achievable by 6 strategies combining, pixelwise, the outputs of 26 unsupervised BGS algorithms, on the CDnet 2014 dataset, both in the ROC space and in terms of the F1 score. The chosen strategies are representative for a large panel of strategies, including both deterministic and non-deterministic ones, voting and learning. In our experiments, we compare our results with the state-of-the-art combinations IUTIS-5 and CNN-SFC, and report six conclusions, among which the existence of an important gap between the performances of the individual algorithms and the best performances achievable by combining them.