ABANDONED OBJECT DETECTION

Electronics and telecommunication engineering

Detecting static objects in video sequences has a high relevance in many surveillance scenarios like airports and railway stations. In this project we propose a system for the detection of static objects in crowded scenes that, based on the detection of two background models learning at different rates. The background is modelled by two mixtures of Gaussians with identical parameters except for the learning rate. The state machine provides the meaning for the interpretation of the results obtained from background subtraction and can be used to incorporate additional information cues, obtaining thus a flexible system especially suitable for real-life applications. The system was built in our surveillance application and successfully validated with several public datasets. We combine short- and long-term background models to extract foreground objects, where each pixel in an input image is classified as a 2-bit code. Subsequently, we introduce a framework to identify static foreground regions based on the temporal transition of code patterns, and to determine whether the candidate regions contain abandoned objects by analyzing the back-traced trajectories of luggage owners.

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