Anomaly detection by using random projection forest

In this paper, we present a novel method for detecting anomalies from surveillance videos, which utilizes the random projection forest for evaluating the rarity of visual clues in a video frame. Given the hierarchical clustering of the data in a random projection tree and the aggregation process in the random forest, we achieve both efficient estimation of incoming samples and improved robustness against under-fitting and over-fitting under improperly selected models. Random forest is also online updatable, which is meaningful for future online anomaly detection. We designed the splitting rule for anomaly detection, the system framework and the criterion of anomaly determination. The efficiency of the proposed methods has been validated by experiments on public UCSD datasets and compared with previously reported results.