Heterogeneous hand gesture recognition using 3D dynamic skeletal data

International audienceHand gestures are the most natural and intuitive non-verbal communication medium while interacting with a computer, and related research efforts have recently boosted interest. Additionally, the identifiable features of the hand pose provided by current commercial inexpensive depth cameras can be exploited in various gesture recognition based systems, especially for Human-Computer Interaction. In this paper, we focus our attention on 3D dynamic gesture recognition systems using the hand pose information. Specifically, we use the natural structure of the hand topology-called later hand skeletal data-to extract effective hand kinematic descriptors from the gesture sequence. Descriptors are then encoded in a statistical and temporal representation using respectively a Fisher kernel and a multi-level temporal pyramid. A linear SVM classifier can be applied directly on the feature vector computed over the whole presegmented gesture to perform the recognition. Furthermore, for early recognition from continuous stream, we introduced a prior gesture detection phase achieved using a binary classifier before the final gesture recognition. The proposed approach is evaluated on three hand gesture datasets containing respectively 10, 14 and 25 gestures with specific challenging tasks. Also, we conduct an experiment to assess the influence of depth-based hand pose estimation on our approach. Experimental results demonstrate the potential of the proposed solution in terms of hand gesture recognition and also for a low-latency gesture recognition. Comparative results with state-of-the-art methods are reported

3D Object Reconstruction from Imperfect Depth Data Using Extended YOLOv3 Network

State-of-the-art intelligent versatile applications provoke the usage of full 3D, depth-based streams, especially in the scenarios of intelligent remote control and communications, where virtual and augmented reality will soon become outdated and are forecasted to be replaced by point cloud streams providing explorable 3D environments of communication and industrial data. One of the most novel approaches employed in modern object reconstruction methods is to use a priori knowledge of the objects that are being reconstructed. Our approach is different as we strive to reconstruct a 3D object within much more difficult scenarios of limited data availability. Data stream is often limited by insufficient depth camera coverage and, as a result, the objects are occluded and data is lost. Our proposed hybrid artificial neural network modifications have improved the reconstruction results by 8.53 which allows us for much more precise filling of occluded object sides and reduction of noise during the process. Furthermore, the addition of object segmentation masks and the individual object instance classification is a leap forward towards a general-purpose scene reconstruction as opposed to a single object reconstruction task due to the ability to mask out overlapping object instances and using only masked object area in the reconstruction process

Hand Posture Recognition with standard webcam for Natural Interaction

This paper presents an experimental prototype designed for natural human-computer interaction in an environmental intelligence system. Using computer vision resources, it analyzes the images captured by a webcam to recognize a person’s hand movements. There is now a strong trend in interpreting these hand and body movements in general, with computer vision, which is a very attractive field of research. In this study, a mechanism for natural interaction was implemented by analyzing images captured by a webcam based on hand geometry and posture, to show its movements in our model. A camera is installed in such a manner that it can discriminate the movements a person makes using Background Subtraction. Then hands are searched for assisted by segmentation by skin color detection and a series of classifiers. Finally, the geometric characteristics of the hands are extracted to distinguish defined control action positions