Generative model for abandoned object detection

This paper proposes an algorithm for abandoned object detection based on generative model of low level features. First, suspected blobs are detected by foreground detection and pixel variance thresholding. Then several low level features on blobs are calculated to remove false alarms, which include pixel variance over time, edge intensity score, edge variance over time, foreground completeness, histogram contrast with respect to surrounding background, bag color model and priors of height and width. The last two features are used as threshold. For other features, their log probability ratio between distributions on positive examples and negative ones are tted by sigmoid function. At last, the fitted log probability ratios are weighted to construct a classifier. The algorithm has been verified in 29 challenging scenes and produces very low false alarms and missing detection.