An approach to recognise 10 elementary gestures is proposed and it can be applied to sign language recognition. In this work, a motion gradient orientation image is extracted directly from a raw video input and transformed to a motion feature vector. This feature vector is then classified into one of the 10 elementary gestures by a sparse Bayesian classifier. A training set of 628 samples and a testing set of over 1000 samples have been obtained to evaluate the proposed method. A real-time system was built and trained with the training set. From the experiment, the reported classification accuracy is 90% and the system can run in around 25 frames per second. Compared with other recently proposed methods that involve the use of hand tracking, the system can work reliably in real-time without relying on accurate tracking, and give a probabilistic output that is useful in complex motion analysis.

Abstract — We describe an interaction paradigm for controlling a robot using hand gestures. In particular, we are interested in the control of an underwater robot by an onsite human operator. Under this context, vision-based control is very attractive, and we propose a robot control and programming mechanism based on visual symbols. A human operator presents engineered visual targets to the robotic system, which recognizes and interprets them. This paper describes the approach and proposes a specific gesture language called “RoboChat”. RoboChat allows an operator to control a robot and even express complex programming concepts, using a sequence of visually presented symbols, encoded into fiducial markers. We evaluate the efficiency and robustness of this symbolic communication scheme by comparing it to traditional gesture-based interaction involving a remote human operator. I.

An approach to recognise and segment 9 elementary gestures from a video input is proposed and it can be applied to continuous sign recognition. An isolated gesture is recognised by first converting a portion of video into a motion gradient orientation image and then classifying it into one of the 9 gestures by a sparse Bayesian classifier. The portion of video used is decided by using a sampling technique based on CONDENSATION framework. By doing so, gestures can be segmented from the video in a probabilistic manner. Experiments show that the proposed method can achieve accuracy around 90 % in both isolated and continuous gesture recognition without using special equipment such as glove devices and the system can run in real-time. 1

Abstract. An approach to increase adaptability of a recognition system, which can recognise 10 elementary gestures and be extended to sign language recognition, is proposed. In this work, recognition is done by firstly extracting a motion gradient orientation image from a raw video input and then classifying a feature vector generated from this image to one of the 10 gestures by a sparse Bayesian classifier. The classifier is designed in a way that it supports online incremental learning and it can be thus re-trained to increase its adaptability to an input captured under a new condition. Experiments show that the accuracy of the classifier can be boosted from less than 40 % to over 80 % by re-training it using 5 newly captured samples from each gesture class. Apart from having a better adaptability, the system can work reliably in real-time and give a probabilistic output that is useful in complex motion analysis. 1

In recent years, research efforts seeking to provide more natural, human-centered means of interacting with computers have gained growing interest. A particularly important direction is that of perceptive user interfaces, where the computer is endowed with perceptive capabilities that allow it to acquire both implicit

Abstract — We present a technique for robust human-robot interaction taking into consideration uncertainty in input and task execution costs incurred by the robot. Specifically, this research aims to quantitatively model confirmation feedback, as required by a robot while communicating with a human operator to perform a particular task. Our goal is to model human-robot interaction from the perspective of risk minimization, taking into account errors in communication, risk involved in performing the required task, and task execution costs. Given an input modality with non-trivial uncertainty, we calculate the cost associated with performing the task specified by the user, and if deemed necessary, ask the user for confirmation. The estimated task cost and the uncertainty measure is given as input to a Decision Function, the output of which is then used to decide whether to execute the task, or request clarification from the user. In cases where the cost or uncertainty (or both) is estimated to be exceedingly high by the system, task execution is deferred until a significant reduction in the output of the Decision Function is achieved. We test our system through human-interface experiments, based on a framework custom designed for our family of amphibious robots, and demonstrate the utility of the framework in the presence of large task costs and uncertainties. We also present qualitative results of our algorithm from field trials of our robots in both open – and closed–water environments. I.

journal homepage: www.elsevier.com/locate/imavis Definition and recovery of kinematic features for recognition of American sign