Abstract. This paper presents a new approach for the detection of abandonedobjects in partially controlled environments using images from a low cost depthsensor. To reach this goal, we propose a systems which involves: (i) a two phaseobject segmentation based on 3D points clustering, (ii) an object selection basedon permanence and object dimensions and, ﬁnally, (iii) a state machinemonitoring capable to deal with occlusions. The proposed system exclusivelyconsiders depth images, which makes it independent of lighting. In order to ob-tain evidences of the system performance in real conditions, we have conductedan experimental study on video sequences captured into a public bus, obtainingsuccessful results. Keywords: abandoned object detection, depth sensor, real conditions. 1 Introduction The growingdemandfor videosurveillancesystems in publicareas involvesan increasein the amount of information to analyse. This increase in the amount of information hasbecome intractable for human supervisors. For this reason, it is particularly interestingto design and implement autonomous systems capable of extracting information fromthese environments and generating alerts automatically. It is expected that displayingthese alerts to a human operator in an understandable language instead of the wholevisual captured information reduces their stress and workload. As a consequence, it isalso expected that it improves their performance.The problem of automatic detection of abandonedobjects in public areas is currentlya research topic of great interest due to the large numberof people concentratedin theseareas and the potential security risks that entails the abandonment of objects. This topichas gained importance in recent years, even entering as an important topic of some in-ternational conferences in video surveillance. As a result, there have been many authorswho have made contributionsin this area using manydifferenttechniques. For example,in [1], authors use a trans-dimensional Markov Chain Monte Carlo tracking approachto separate moving and static blobs. This system can also separate static blobs in at-tended and unattended objects by the analysis of the behaviour of moving blobs. Animportant and remarkable issue regarding the abandoned object detection topic is thatmost of the works are heavily based on background subtraction methods, using adap-tive background models, and the analysis of changes of the foregroundand background
['Antonio Collazos', 'David Fernández-López', 'Antonio S. Montemayor', 'Juan José Pantrigo', 'María Luisa Delgado']
Abandoned Object Detection on Controlled Scenes Using Kinect