Removing nuisance in tracklet data
|N. Shepeleva, T. Hoch, L. Fischer, W. Kloihofer, B. Moser. Removing nuisance in tracklet data. volume 10802, pages 08020S, DOI 10.1117/12.2325636, 11, 2018.|
|Buch||Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies II - Proc. SPIE 2018|
In this article, the problem of the lack of robustness and reliability of surveillance systems through disturbing security irrelevant events such as tree shaking, birds flying, etc. is tackled. A novel scene analysis approach based on hypergraph-based trajectories is introduced for reducing the rate of false positives. The conception of hypergraph-based trajectories relaxes the notion of point-based trajectories by allowing multiple incidences between subsequent points in time. This allows a principled approach for the extraction of robust features based on bounding boxes resulting from existing 3rd party detection methods. The experimental part is based on data collected from single-view camera systems over a two-year non-stop recording in the frame of the Austrian KIRAS project SKIN1 on protecting critical infrastructure. The results show substantial reduction of irrelevant false alarms, hence improving the overall system’s performance.