No Matter Where You Are: Flexible Graph-Guided Multi-task Learning for Multi-view Head Pose Classification under Target Motion

We propose a novel Multi-Task Learning framework (FEGA-MTL) for classifying the head pose of a person who moves freely in an environment monitored by multi-ple, large field-of-view surveillance cameras. As the tar-get (person) moves, distortions in facial appearance ow-ing to camera perspective and scale severely impede per-formance of traditional head pose classification methods. FEGA-MTL operates on a dense uniform spatial grid and learns appearance relationships across partitions as well as partition-specific appearance variations for a given head pose to build region-specific classifiers. Guided by two graphs which a-priori model appearance similarity among (i) grid partitions based on camera geometry and (ii) head pose classes, the learner efficiently clusters appearance-wise related grid partitions to derive the optimal partition-ing. For pose classification, upon determining the target’s position using a person tracker, the appropriate region-specific classifier is invoked. Experiments confirm that FEGA-MTL achieves state-of-the-art classification with few training data. 1

A Multi-task Learning Framework for Head Pose Estimation under Target Motion

Recently, head pose estimation (HPE) from low-resolution surveillance data has gained in importance. However, monocular and multi-view HPE approaches still work poorly under target motion, as facial appearance distorts owing to camera perspective and scale changes when a person moves around. To this end, we propose FEGA-MTL, a novel framework based on Multi-Task Learning (MTL) for classifying the head pose of a person who moves freely in an environment monitored by multiple, large field-of-view surveillance cameras. Upon partitioning the monitored scene into a dense uniform spatial grid, FEGA-MTL simultaneously clusters grid partitions into regions with similar facial appearance, while learning region-specific head pose classifiers. In the learning phase, guided by two graphs which a-priori model the similarity among (1) grid partitions based on camera geometry and (2) head pose classes, FEGA-MTL derives the optimal scene partitioning and associated pose classifiers. Upon determining the target's position using a person tracker at test time, the corresponding region-specific classifier is invoked for HPE. The FEGA-MTL framework naturally extends to a weakly supervised setting where the target's walking direction is employed as a proxy in lieu of head orientation. Experiments confirm that FEGA-MTL significantly outperforms competing single-task and multi-task learning methods in multi-view settings

A system for probabilistic joint 3D head tracking and pose estimation in low-resolution, multi-view environments

We present a new system for 3D head tracking and pose estimation in low-resolution, multi-view environments. Our approach consists of a joint particle filter scheme, that combines head shape evaluation with histograms of oriented gradients and pose estimation by means of artificial neural networks. The joint evaluation resolves previous problems of automatic alignment and multi-sensor fusion and gains an automatic system that is flexible against modifications in the available number of cameras. We evaluate on the CLEAR07 dataset for multi-view head pose estimation and achieve mean pose errors of 7.2° and 9.3° for pan and tilt respectively, which improves accuracy compared to our previous work by 14.9% and 25.8%

Mensch-Maschine-Systeme : Wissenschaftliches Kolloquium 5. März 2009, Fraunhofer IITB

Das informationstechnische Kolloquium Mensch-Maschine-Systeme des Fraunhofer IITB gibt anlässlich des 80sten Geburtstags seines ehemaligen Leiters und Präsidenten der Fraunhofer-Gesellschaft, Prof. Dr. Max Syrbe, einen Überblick über Geschichte, Stand und Trends der angewandten Forschung auf dem Gebiet der Mensch-Maschine-Systeme, welcher sich von der Ethik der Techniknutzung über die Automatisierungssysteme bis zu dem Maschinensehen für die Mensch-Maschine-Interaktion erstreckt

Mensch-Maschine-Systeme : Wissenschaftliches Kolloquium 5. März 2009, Fraunhofer IITB

Das informationstechnische Kolloquium Mensch-Maschine-Systeme des Fraunhofer IITB gibt anlässlich des 80sten Geburtstags seines ehemaligen Leiters und Präsidenten der Fraunhofer-Gesellschaft, Prof. Dr. Max Syrbe, einen Überblick über Geschichte, Stand und Trends der angewandten Forschung auf dem Gebiet der Mensch-Maschine-Systeme, welcher sich von der Ethik der Techniknutzung über die Automatisierungssysteme bis zu dem Maschinensehen für die Mensch-Maschine-Interaktion erstreckt