Erratum to: Robust Pose Recognition of the Obscured Human Body

Human Body Pose Recognition Using Spatio-Temporal Templates

We present a novel approach to detecting human silhouettes in monocular sequences that achieves very low rates of both false positives and negatives by combining shape and motion information. To this end, we use sequences of moving silhouettes built using motion capture data that we match against short image sequences. We demonstrate the effectiveness of our technique using both indoor and outd...

Towards Pose-robust Face Recognition on Video

Pose Robust Video - Based Face Recognition

Researchers have been working on human face recognition for decades. Face recognition is hard due to different types of variations in face images, such as pose, illumination and expression, among which pose variation is the hardest one to deal with. To improve face recognition, this thesis presents an integrated approach to performing pose robust video-based face tracking and recognition by usi...

Combining Human Body Shape and Pose Estimation for Robust Upper Body Tracking Using a Depth Sensor

Rapid and accurate estimation of a person’s upper body shape and real-time tracking of the pose in the presence of occlusions is crucial for many future assistive technologies, health care applications and telemedicine systems. We propose to tackle this challenging problem by combining data-driven and generative methods for both body shape and pose estimation. Our strategy comprises a subspace-...

Mobile Robot Aided Silhouette Imaging and Robust Body Pose Recognition for Elderly-Fall Detection

This article introduces a mobile infrared silhouette imaging and sparse representation-based pose recognition for building an elderly-fall detection system. The proposed imaging paradigm exploits the novel use of the pyroelectric infrared (PIR) sensor in pursuit of body silhouette imaging. A mobile robot carrying a vertical column of multi-PIR detectors is organized for the silhouette acquisiti...