Tracking the articulated motion of two strongly interacting hands

Oikonomidis, Iason; Kyriazis, Nicholas; Argyros, Antonis A.

doi:10.1109/cvpr.2012.6247885

Cited by 212 publications

(216 citation statements)

References 18 publications

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“…Moreover, a common approach for learning both the interaction that involves objects (Oikonomidis et al, 2013;Kyriazis and Argyros, 2014) and object affordances (Koppula et al, 2013;Koppula and Saxena, 2014), from RGB-D data, is to utilize a tracking algorithm for tracking the hand movement of a human user. The learning method that we have suggested in this work is solely based on the tracked movements of object entities.…”

Section: Discussionmentioning

confidence: 99%

Learning Actions to Improve the Perceptual Anchoring of Objects

2017

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In this paper, we examine how to ground symbols referring to objects in perceptual data from a robot system by examining object entities and their changes over time. In particular, we approach the challenge by (1) tracking and maintaining object entities over time; and (2) utilizing an artificial neural network to learn the coupling between words referring to actions and movement patterns of tracked object entities. For this purpose, we propose a framework that relies on the notations presented in perceptual anchoring. We further present a practical extension of the notation such that our framework can track and maintain the history of detected object entities. Our approach is evaluated using everyday objects typically found in a home environment. Our object classification module has the possibility to detect and classify over several 100 object categories. We demonstrate how the framework creates and maintains, both in space and time, representations of objects such as "spoon" and "coffee mug." These representations are later used for training of different sequential learning algorithms to learn movement actions such as "pour" and "stir." We finally exemplify how learned movements actions, combined with commonsense knowledge, further can be used to improve the anchoring process per se.

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Section: Discussionmentioning

confidence: 99%

Learning Actions to Improve the Perceptual Anchoring of Objects

2017

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“…(4)) is performed based on Particle Swarm Optimization (PSO) [19] which is a stochastic, evolutionary optimization method. It has been demonstrated that PSO is a very effective and efficient method for solving other vision optimization problems such as head pose estimation [23], hand articulation tracking [22] and others. PSO achieves optimization based on the collective behavior of a set of particles (candidate solutions) that evolve in runs called generations.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Vision-Based SLAM and Moving Objects Tracking for the Perceptual Support of a Smart Walker Platform

Panteleris

Argyros

2015

Computer Vision - ECCV 2014 Workshops

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Abstract. The problems of vision-based detection and tracking of independently moving objects, localization and map construction are highly interrelated, in the sense that the solution of any of them provides valuable information to the solution of the others. In this paper, rather than trying to solve each of them in isolation, we propose a method that treats all of them simultaneously. More specifically, given visual input acquired by a moving RGBD camera, the method detects independently moving objects and tracks them in time. Additionally, the method estimates the camera (ego)motion and the motion of the tracked objects in a coordinate system that is attached to the static environment, a map of which is progressively built from scratch. The loose assumptions that the method adopts with respect to the problem parameters make it a valuable component for any robotic platform that moves in a dynamic environment and requires simultaneous tracking of moving objects, egomotion estimation and map construction. The usability of the method is further enhanced by its robustness and its low computational requirements that permit real time execution even on low-end CPUs.

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“…Indeed, rather few works explicitly model the physical collision between objects. Oikonomidis (2012) tracks two interacting hands with Kinect input, introducing a penalty term measuring the inter-penetration of fingers to invalidate impossible articulated poses. Both Oikonomidis et al (2011b) and Kyriazis and Argyros (2013) track a hand and moving object simultaneously, and invalid configurations similarly penalized.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Tracking of Single and Multiple Objects from Depth-Colour Imagery Using 3D Signed Distance Functions

et al. 2017

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We describe a novel probabilistic framework for real-time tracking of multiple objects from combined depthcolour imagery. Object shape is represented implicitly using 3D signed distance functions. Probabilistic generative models based on these functions are developed to account for the observed RGB-D imagery, and tracking is posed as a maximum a posteriori problem. We present first a method suited to tracking a single rigid 3D object, and then generalise this to multiple objects by combining distance functions into a shape union in the frame of the camera. This second model accounts for similarity and proximity between objects, and leads to robust real-time tracking without recourse to bolt-on or ad-hoc collision detection.

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Tracking the articulated motion of two strongly interacting hands

Cited by 212 publications

References 18 publications

Learning Actions to Improve the Perceptual Anchoring of Objects

Learning Actions to Improve the Perceptual Anchoring of Objects

Vision-Based SLAM and Moving Objects Tracking for the Perceptual Support of a Smart Walker Platform

Real-Time Tracking of Single and Multiple Objects from Depth-Colour Imagery Using 3D Signed Distance Functions

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