We present a method to learn models of human heads for the purpose of detection from different viewing angles. We focus on a model where objects are represented as constellations ...
We propose a bottom-up human detector that can deal with arbitrary poses and viewpoints. Heads, limbs and torsos are individually detected, and an efficient assembly strategy is u...
In this paper we address the problem of recognising interactions between two people in realistic scenarios for video retrieval purposes. We develop a per-person descriptor that us...
Alonso Patron, Marcin Marszalek, Andrew Zisserman,...
We present a new scheme to robustly detect a type of human attentive behavior, which we call frequent change in focus of attention (FCFA), from video sequences. FCFA behavior can b...
This paper addresses the problem of estimating head pose over a wide range of angles from low-resolution images. Faces are detected using chrominance-based features. Grey-level nor...