Reliable Left Luggage Detection Using Stereo Depth and Intensity Cues

Abstract

Reliable and timely detection of abandoned items in public places still represents an unsolved problem for automated visual surveillance. Typical surveilled scenarios are associated with high visual ambiguity such as shadows, occlusions, illumination changes and substantial clutter consisting of a mixture of dynamic and stationary objects. Motivated by these challenges we propose a reliable left item detection approach based on the combination of intensity and depth data from a passive stereo setup. The employed in-house developed stereo system consists of low-cost sensors and it is capable to perform detection in environments of up to 10m x 10m in size. The proposed algorithm is tested on a set of indoor sequences and compared to manually annotated ground truth data. Obtained results show that many failure modes of intensity-based approaches are absent and even small-sized objects such as a handbag can be reliably detected when left behind in a scene. The presented results display a very promising approach, which can robustly detect left luggage in dynamic environments at a close to real-time computational speed.