An agent-based approach for tracking people in indoor complex environments

This paper presents an agent-based architecture designed to functionally combine data from an homogeneous network of sensors for tracking purposes. The system has been developed in a video surveillance context to detect, classify and track moving objects in a scene of interest. Although single camera systems could perform the tasks outlined above, they would not be able to deal with topologically complex environments such as corridor, corners and indoor locations in general. The multi-sensor approach has been used to overcome these problems, nevertheless issues arise such as data fusion, synchronization and camera calibration. The sensor fusion approach here purposed uses autonomous software agents to negotiate the combination of data and the fusion is carried out by appropriate signal processing algorithms. The system has been tested with indoor video sequences to show the system's ability to preserve identity and of correct trajectory estimation of the tracked object.