Gravity optimised particle filter for hand tracking

This paper presents a gravity optimised particle filter (GOPF) where the magnitude of the gravitational force for every particle is proportional to its weight. GOPF attracts nearby particles and replicates new particles as if moving the particles towards the peak of the likelihood distribution, improving the sampling efficiency. GOPF is incorporated into a technique for hand features tracking. A fast approach to hand features detection and labelling using convexity defects is also presented. Experimental results show that GOPF outperforms the standard particle filter and its variants, as well as state-of-the-art CamShift guided particle filter using a significantly reduced number of particles

Capturing Hands in Action using Discriminative Salient Points and Physics Simulation

Hand motion capture is a popular research field, recently gaining more attention due to the ubiquity of RGB-D sensors. However, even most recent approaches focus on the case of a single isolated hand. In this work, we focus on hands that interact with other hands or objects and present a framework that successfully captures motion in such interaction scenarios for both rigid and articulated objects. Our framework combines a generative model with discriminatively trained salient points to achieve a low tracking error and with collision detection and physics simulation to achieve physically plausible estimates even in case of occlusions and missing visual data. Since all components are unified in a single objective function which is almost everywhere differentiable, it can be optimized with standard optimization techniques. Our approach works for monocular RGB-D sequences as well as setups with multiple synchronized RGB cameras. For a qualitative and quantitative evaluation, we captured 29 sequences with a large variety of interactions and up to 150 degrees of freedom.Comment: Accepted for publication by the International Journal of Computer Vision (IJCV) on 16.02.2016 (submitted on 17.10.14). A combination into a single framework of an ECCV'12 multicamera-RGB and a monocular-RGBD GCPR'14 hand tracking paper with several extensions, additional experiments and detail

Multi-touchless: Real-time fingertip detection and tracking using geodesic maxima

Since the advent of multitouch screens users have been able to interact using fingertip gestures in a two dimensional plane. With the development of depth cameras, such as the Kinect, attempts have been made to reproduce the detection of gestures for three dimensional interaction. Many of these use contour analysis to find the fingertips, however the success of such approaches is limited due to sensor noise and rapid movements. This paper discusses an approach to identify fingertips during rapid movement at varying depths allowing multitouch without contact with a screen. To achieve this, we use a weighted graph that is built using the depth information of the hand to determine the geodesic maxima of the surface. Fingertips are then selected from these maxima using a simplified model of the hand and correspondence found over successive frames. Our experiments show real-time performance for multiple users providing tracking at 30fps for up to 4 hands and we compare our results with stateof- the-art methods, providing accuracy an order of magnitude better than existing approaches. © 2013 IEEE