Perspective and appearance context for people surveillance in open areas

Abstract

Contextual information can be used both to reduce computations and to increase accuracy and this paper presents how it can be exploited for people surveillance in terms of perspective (i.e. weak scene calibration) and appearance of the objects of interest (i.e. relevance feedback on the training of a classifier). These techniques are applied to a pedestrian detector that exploits covariance descriptors through a LogitBoost classifier on Riemannian manifolds. The approach has been tested on a construction working site where complexity and dynamics are very high, making human detection a real challenge. The experimental results demonstrate the improvements achieved by the proposed approach.