Perspective and Appearance Context for People Surveillance in Open Areas

Contextual information can be used both to reduce computationsand to increase accuracy and this paper presentshow it can be exploited for people surveillance in terms ofperspective (i.e. weak scene calibration) and appearance ofthe objects of interest (i.e. relevance feedback on the trainingof a classifier). These techniques are applied to a pedestriandetector that exploits covariance descriptors througha LogitBoost classifier on Riemannian manifolds. The approachhas been tested on a construction working site wherecomplexity and dynamics are very high, making human detectiona real challenge. The experimental results demonstratethe improvements achieved by the proposed approach.