In video surveillance, classication of visual data can be very hard, due to the scarce resolution and the noise characterizing the sensors' data. In this paper, we propose a novel feature, the ARray of COvariances (ARCO), and a multi-class classifcation framework operatingon Riemannian manifolds. ARCO is composed by a structure of covariance matrices of image features, able to extract information from data at prohibitive low resolutions. The proposed classication framework consists in instantiating a new multi-class boosting method, working on the manifold Sym+ of symmetric positive defnite d x d (covariance) matrices. As practical applications, we consider different surveillance tasks, such as head pose classifcation and pedestrian detection, providing
novel state-of-the-art performances on standard datasets.
|Title of host publication||Computer Vision - ECCV 2010|
|Number of pages||14|
|Publication status||Published - 2010|
|Name||LECTURE NOTES IN COMPUTER SCIENCE|
- Riemannian Manifolds, Video Surveillance, Multi-class Boosting, Head Pose Estimation, Pedestrian Detection