This paper presents a method for the automated detection of dropped objects in surveillance scenarios, which is a very important task for abandoned object detection. Our method works in single views and exploits prior information of the scene, such as geometry or the fact that a number of false alarms are caused by known objects, such as humans. The proposed approach builds dropped object candidates by analyzing blobs obtained with a multi-layer background subtraction approach. The created dropped object candidates are then characterized both by appearance and by temporal aspects such as the estimated drop time. Next, we incorporate prior knowledge about the possible sizes and positions of dropped objects through an efficient filtering approach. Finally, the output of a human detector is exploited over in order to filter out static objects that are likely to be humans that remain still. Experimental results on the publicly available PETS2006 datasets and on several long sequences recorded in metro stations show the effectiveness of the proposed approach. Furthermore, our approach can operate in real-time.