This paper is about structuring an object recognition task for finely controlling computational costs. The motivating problem comes from 3D scans with many identical objects, from which the pose of the objects must be recovered as accurately as possible with a given computational budget. This paper presents a method for detecting such objects which allows control over the amount of computation ...

We present a template-based approach to detecting human silhouettes in a specific walking pose. Our templates consist of short sequences of 2D silhouettes obtained from motion capture data. This lets us incorporate motion information into them and helps distinguish actual people who move in a predictable way from static objects whose outlines roughly resemble those of humans. Moreover, during t...

We describe a system for human body pose estimation from multiple views that is fast and not dependent on a rigid 3D model. We make use of recent work in decomposition of a silhouette into 2D parts. These 2D part primitives are matched across views to build assemblies in 3D. In order to search for the best assembly, we use a likelihood function that integrates information available from multipl...

This paper describes a computer vision-based approach to body pose estimation. The algorithm can be executed in real-time and processes low resolution, monocular image sequences. A silhouette is extracted and matched against a projection of a 16 DOF human body model. In addition, skin color is used to locate hands and head. No detailed human body model is needed. We evaluate the approach both q...

Imitation is considered to be a kind of social learning that allows the transfer of information, actions, behaviors, etc. Whereas current robots are unable to perform as many tasks as human, it is a natural way for them to learn by imitations, just as human does. With the humanoid robots being more intelligent, the field of robot imitation has getting noticeable advance. In this paper, we focus...

Our objective is to efficiently and accurately estimate human upper body pose in gesture videos. To this end, we build on the recent successful applications of random forests (RF) classifiers and regressors, and develop a pose estimation model with the following novelties: (i) the joints are estimated sequentially, taking account of the human kinematic chain. This means that we don’t have to ma...

A novel body pose based human tracking model is proposed for pedestrian tracking. This work investigates the challenges of reliable pedestrian tracking and proposes an improved model under challenging environments. Specifically, it claims that it is useful to exploit the curvature information of different body poses in tracking framework to overcome general tracking problems. In this paper diff...

Ming-Ching Chang1,2 [email protected] Honggang Qi1,3 [email protected] Xin Wang1 [email protected] Hong Cheng4 [email protected] Siwei Lyu1 [email protected] 1 Computer Science Department University at Albany, State University of New York Albany, USA 2 Computer Vision Lab GE Global Research Center Niskayuna, USA 3 University of Chinese Academy of Sciences Beijing, China 4 Center for Robotics Univers...

In this project, I utilized probabilistic graphical models together with discriminative models such as SVM to perform upper body pose estimation. Figure 1 illustrates the definition of a problem in terms of input and output. Pose estimation is an important problem that has a wide array of applications such as pedestrian detection, sports video analysis, action recognition, etc. The entire human...

We report on a continuous time odometry scheme for a walking hexapod robot built upon a previously developed leg-strain based body pose estimator. We implement this estimation procedure and odometry scheme on the robot RHex and evaluate its performance at widely varying speeds and over different ground conditions by means of a 6 degree of freedom vision based ground truth measurement system (GT...