Automatic recognition and prediction of in-vehicle human activities has a significant impact on the next generation driver assistance intelligent autonomous vehicles. In this article, we present novel single image action algorithm inspired by perception that often focuses selectively parts images to acquire information at specific places which are distinct given task. Unlike existing approaches...

With the popularization of game and VR/AR devices, there is a growing need for capturing human motion with sparse set tracking data. In this paper, we introduce deep neural-network (DNN) based method real-time prediction lower-body pose only from signals upper-body joints. Specifically, our Gated Recurrent Unit (GRU)-based recurrent architecture predicts feet contact probability past sequence h...

An approach for estimating 3D body pose from multiple, uncalibrated views is proposed. First, a mapping from image features to 2D body joint locations is computed using a statistical framework that yields a set of several body pose hypotheses. The concept of a “virtual camera” is introduced that makes this mapping invariant to translation, image-plane rotation, and scaling of the input. As a co...

This paper addresses the problem of automatic detection and recovery of three-dimensional human body pose from monocular video sequences for HCI applications. We propose a new hierarchical part-based pose estimation method for the upper-body that efficiently searches the high dimensional articulation space. The body is treated as a collection of parts linked in a kinematic structure. Search for...

Abstract: This paper presents a method for 3D Human Body pose estimation by using a multi-camera system. The pose is estimated by RANSAC-object search with a robust least square fitting of 3D points to SuperQuadric (SQ) models of the searched object. The solution is verified by evaluating the matching score between the SQ object model and 3D real data captured by a multi-camera system and segme...

Imagery data is an important component of multimedia content and appears commonly in the Internet domain, TV programs and movies. Analysis and interpretation of imagery data is therefore an important research area in IMSC. The project focuses on the human body, which is the most interesting object, and aims to develop techniques for estimating the body pose automatically. Potential applications...

Faces and bodies are complex structures, perception of which can play important roles in person identification and inference of emotional state. Face representations have been explored using behavioural adaptation: in particular, studies have shown that face aftereffects show relatively broad tuning for viewpoint, consistent with origin in a high-level structural descriptor far removed from the...

This chapter presents a novel approach to markerless real-time 3D pose estimation in a multi-camera setup. We explain how foreground-background segmentation and 3D reconstruction are used to extract a 3D hull of the user.This is done in real time using voxel carving and a fixed lookup table.The body pose is then retrieved using an example-based classifier that uses 3D Haar-like wavelet features...

A nonlinear supervised learning model, the Specialized Mappings Architecture (SMA), is described and applied to the estimation of human body pose from monocular images. The SMA consists of several specialized forward mapping functions and an inverse mapping function. Each specialized function maps certain domains of the input space (image features) onto the output space (body pose parameters). ...