Automatic object labelling for monitored environments using clustering techniques

A new algorithm to classify moving objects in monitored environments is presented. The approach is based on a supervised machine learning algorithm and uses as input data the results obtained in a previously developed segmentation algorithm. The algorithm has a training stage which uses clustering algorithms and a learning stage to learn the features of each kind of object and to be able to classify moving objects in video scenes. The labelling approach is focused on video-surveillance monitoring, thus it runs in real-time, and it has been designed to exploit different features of the objects: position, size, shape and motion. Experimental results show promising performance in terms of both accuracy and efficiency.