We introduce a real-time method that estimates the 3D human pose directly in the popular Bio Vision Hierarchy (BVH) format, given estimations of the 2D body joints originating from monocular color images. Our contributions include: (a) A novel and compact 2D pose representation. (b) A human body orientation classifier and an ensemble of orientation-tuned neural networks that regress the 3D human pose by also allowing for the decomposition of the body to an upper and lower kinematic hierarchy. This permits the recovery of the human pose even in the case of significant occlusions. (c) An efficient Inverse Kinematics solver that refines the neural-network-based solution providing 3D human pose estimations that are consistent with the limb sizes of a target person (if known). All the above yield a 33% accuracy improvement on the Human 3.6 Million (H3.6M) dataset compared to the baseline method (MocapNET) while maintaining real-time performance (70 fps in CPU-only execution).

In this article, we present results obtained from field trials with the Hobbit robotic platform, an assistive, social service robot aiming at enabling prolonged independent living of older adults in their own homes. Our main contribution lies within the detailed results on perceived safety, usability, and acceptance from field trials with autonomous robots in real homes of older users. In these field trials, we studied how 16 older adults (75 plus) lived with autonomously interacting service robots over multiple weeks. Robots have been employed for periods of months previously in home environments for older people, and some have been tested with manipulation abilities, but this is the first time a study has tested a robot in private homes that provided the combination of manipulation abilities, autonomous navigation, and non-scheduled interaction for an extended period of time. This article aims to explore how older adults interact with such a robot in their private homes. Our results show that all users interacted with Hobbit daily, rated most functions as well working, and reported that they believe that Hobbit will be part of future elderly care. We show that Hobbit’s adaptive behavior approach towards the user increasingly eased the interaction between the users and the robot. Our trials reveal the necessity to move into actual users’ homes, as only there, we encounter real-world challenges and demonstrate issues such as misinterpretation of actions during non-scripted human-robot interaction.

This work demonstrates a real-time 3D hand tracking application that runs via computation offloading. The proposed framework enables the application to run on low-end mobile devices such as laptops and tablets, despite the fact that they lack the sufficient hardware to perform the required computations locally. The network connection takes the place of a GPGPU accelerator and sharing resources with a larger workstation becomes the acceleration mechanism. The unique properties of a generative optimizer are examined and constitute a challenging use-case, since the requirement for real-time performance makes it very latency-sensitive.

We propose a new hybrid method for 3D human body pose estimation based on RGBD data. We treat this as an optimization problem that is solved using a stochastic optimization technique. The solution to the optimization problem is the pose parameters of a human model that register it to the available observations. Our method can make use of any skinned, articulated human body model. However, we focus on personalized models that can be acquired easily and automatically based on existing human scanning and mesh rigging techniques. Observations consist of the 3D structure of the human (measured by the RGBD camera) and the body joints locations (computed based on a dis-criminative, CNN-based component). A series of quantitative and qualitative experiments demonstrate the accuracy and the benefits of the proposed approach. In particular, we show that the proposed approach achieves state of the art results compared to competitive methods and that the use of personalized body models improve significantly the accuracy in 3D human pose estimation.

3D tracking of objects and hands in an object manipulation scenario is a very interesting computer vision problem with a wide variety of applications ranging from consumer electronics to robotics and medicine. Recent advances in this research topic allow for 3D tracking of complex scenarios involving bi-manual manipulation of several rigid objects using commodity hardware and with high accuracy. The problem with these approaches is that they treat tracking as a search problem whose dimensionality increases with the number of objects in the scene. This fact typically limits the number of the tracked objects and/or the processing framerate. In this paper we present a method that utilizes simple low level motion cues for dynamically assigning computational resources to parts of the scene where they are actually required. In a series of experiments, we show that this simple idea improves tracking performance dramatically at a cost of only a minor degradation of tracking accuracy. The works that are most related to ours are the approaches by Kyriazis and Argyros on top-down 3D tracking of multiple active objects from RGBD input [1, 2]. The methodological part of our contribution can be briefly described as an extra processing node in the pipeline of [2] which it extends. The tracking approach in [2], the Ensemble of Collaborative Trackers (ECT), regards a set of semi-independent trackers. Each tracker is associated with a distinct object in the scene. For an object to be tracked, a separate optimization problem is solved, one for each frame and each object. Each optimization problem is numerically solved using a black box optimizer, i.e. a variant of the Particle Swarm Optimization (PSO) algorithm. PSO treats the objective function as an oracle and queries it on purposefully evolved “guesses” in the search space, in order to find the optimum. The harder the problem is, the more guesses (budget, which is the product of PSO particles and PSO generations that need to be computed) are required for adequate accuracy to be achieved. For example, tracking the pose and the articulation of the hand amounts to solving a 27-parameter optimization problem. This is much harder than solving for the 3D pose of a rigid object (6 parameters). ECT [2] assigns a fixed amount of computational resources to each tracking sub-problem which depends on this notion of complexity and which is empirically estimated. In this work, we quantify at run time, how hard the tracking of an object should be, not only based on its intrinsic complexity, but also based on its observed dynamics. An object that appears static in the recent temporal window requires less resources compared to an object whose state is more dynamic. Thus, change detection is performed on the image space of color intensities and depth measurements of the RGBD input. In more detail, for the next tracking frame, and the ith tracker, a value mi is computed, which takes the value of 0 if the corresponding tracked object appears to be relatively static, and takes the value of 1 otherwise. For mi to be 1, any of the following need to be true: • the mean value of the pixel-wise differences between the observations of object i and the back-projection of the last estimated configuration of object i is high enough, • the kinetic energy of object i, as computed from so far tracked velocities, suggests a moving object, • the amount of missing depth measurements changes substantially, from one frame to the next, which might me attributed to change in the slant of object i, or • any of the above was satisfied in the recent past (damping). Budget has the trivial minimum value of 0. Moreover, as it has been shown in [3, 4], a PSO budget of 64 particles and 64 generations suffices to track a hand, even in interaction with another hand. No more budget should be required to track simpler structures such as rigid objects. The proposed dynamic budget allocation policy assigns a minimum budget Bmin of 64 particles running for 4 generations to the objects that are static and a maximum budget Bmax that never exceeds the aforementioned (a) Pouring pancake mix (b) Waiting for the mix to cook

We present a novel, non-intrusive approach for estimating contact forces during hand-object interactions relying solely on visual input provided by a single RGB-D camera. We consider a manipulated object with known geometrical and physical properties. First, we rely on model-based visual tracking to estimate the object's pose together with that of the hand manipulating it throughout the motion. Following this, we compute the object's first and second order kinematics using a new class of numerical differentiation operators. The estimated kinematics is then instantly fed into a second-order cone program that returns a minimal force distribution explaining the observed motion. However, humans typically apply more forces than mechanically required when manipulating objects. Thus, we complete our estimation method by learning these excessive forces and their distribution among the fingers in contact. We provide a full validity analysis of the proposed method by evaluating it based on ground truth data from additional sensors such as accelerometers, gyroscopes and pressure sensors. Experimental results show that force sensing from vision (FSV) is indeed feasible.