Robust Foreground and Abandonment Analysis for Large-Scale Abandoned Object Detection in Complex Surveillance Videos

We present a robust system for large-scale abandoned object detection (AOD) with low false positive rates and good detection accuracy under complex realistic scenarios. The robustness of our system is largely attributed to an approach we develop for foreground analysis, which can effectively differentiate foreground objects from background under challenging conditions such as lighting changes, low textureness and low contrast as well as cluttered background. This significantly eliminates false positives caused by lighting changes while retaining true drops better. We further perform abandonment analysis to reduce more false positives including those related to people, at a small cost of accuracy (≤ 2%). We demonstrate the effectiveness of our approach on two large data sets collected in various challenging scenes, providing detailed analysis of experiments.