Detection of objects carried by people

Our application context is the visual surveillance of archeological sites. In this context the main aim is to detect the presence of people and to scan them in order to recognize intruders on the basis of their gestures. Since an intruder needs some utensils indispensable to perform the illegal actions of excavating on the ancient ruins, intruder detection involves first of all to ascertain if a person is carrying some objects and then recognizing the kind. In this paper we concentrate on the recognition of the objects carried by the detected moving persons. An example-based learning technique is used to first detect people and successively to scan them to recognize the possible carried objects. The patterns to be analysed are represented through the approximation coefficients of their three level wavelet decomposition. Pattern classification is performed through a supervised three layer neural network.