An edge-based method for effective abandoned luggage detection in complex surveillance videos

Abandoned objects detection is one of the most challenging tasks in intelligent video surveillance systems. In this paper we present a new method for detecting abandoned objects (AO) using edges instead of pixel intensities. Our main focus, is on reducing false alarms, while keeping a high positives detection rate. Based on edge information, the proposed method reduces errors rate considerably compared to pixel intensities based approaches. At first, static edges are detected by applying a temporal accumulation step using the foreground edges mask resulting from the edge-based background subtraction model. Then, edges clustering is applied on the obtained stable edges mask, using edges' position and stability in time to delineate the object bounding box. Finally, an efficient classification approach, relying on edges' position, orientation, and staticness based scores, is applied on the AO candidates. The proposed approach has been validated on several challenging benchmarks, and is compared to other works of the literature. The results shows that our method reduce false alarms rates greatly, while keeping a good detection accuracy of true positives.