Detection and representation of moving objects for video surveillance

In this dissertation two new approaches have been introduced for the automatic detection of moving objects (such as people and vehicles) in video surveillance sequences. The first technique analyses the original video and exploits spatial and temporal information to find those pixels in the images that correspond to moving objects. The second technique analyses video sequences that have been encoded according to a recent video coding standard (H.264/AVC). As such, only the compressed features are analyzed to find moving objects. The latter technique results in a very fast and accurate detection (up to 20 times faster than the related work). Lastly, we investigated how different XML-based metadata standards can be used to represent information about these moving objects. We proposed the usage of Semantic Web Technologies to combine information described according to different metadata standards

Effect of H.264/AVC compression on object detection for video surveillance

Nowadays, video surveillance systems apply video compression to reduce bandwith and storage cost. However, generally, these video sequences are the input, after decoding, for video analysis modules. H.264/AVC is the newest video standard and is assumed to be omnipresent in video surveillance systems in the near future. Since the video compression introduces artefacts, which influence the performance of these analysis modules, it is important to make a quantitative evaluation of this effect. Hence, in this paper we present the first quantitative analysis of the effect that H.264/AVC compression has upon a generally accepted moving object detection technique. We analyze different encoding schemes and show the influence on the object detection results for different representative sequences