Analysis of Hand Segmentation in the Wild

A large number of works in egocentric vision have concentrated on action and object recognition. Detection and segmentation of hands in first-person videos, however, has less been explored. For many applications in this domain, it is necessary to accurately segment not only hands of the camera wearer but also the hands of others with whom he is interacting. Here, we take an in-depth look at the hand segmentation problem. In the quest for robust hand segmentation methods, we evaluated the performance of the state of the art semantic segmentation methods, off the shelf and fine-tuned, on existing datasets. We fine-tune RefineNet, a leading semantic segmentation method, for hand segmentation and find that it does much better than the best contenders. Existing hand segmentation datasets are collected in the laboratory settings. To overcome this limitation, we contribute by collecting two new datasets: a) EgoYouTubeHands including egocentric videos containing hands in the wild, and b) HandOverFace to analyze the performance of our models in presence of similar appearance occlusions. We further explore whether conditional random fields can help refine generated hand segmentations. To demonstrate the benefit of accurate hand maps, we train a CNN for hand-based activity recognition and achieve higher accuracy when a CNN was trained using hand maps produced by the fine-tuned RefineNet. Finally, we annotate a subset of the EgoHands dataset for fine-grained action recognition and show that an accuracy of 58.6% can be achieved by just looking at a single hand pose which is much better than the chance level (12.5%).Comment: Accepted at CVPR 201

Lightweight real-time hand segmentation leveraging MediaPipe landmark detection

Producción CientíficaReal-time hand segmentation is a key process in applications that require human–computer interaction, such as gesture rec- ognition or augmented reality systems. However, the infinite shapes and orientations that hands can adopt, their variability in skin pigmentation and the self-occlusions that continuously appear in images make hand segmentation a truly complex problem, especially with uncontrolled lighting conditions and backgrounds. The development of robust, real-time hand segmentation algorithms is essential to achieve immersive augmented reality and mixed reality experiences by correctly interpreting collisions and occlusions. In this paper, we present a simple but powerful algorithm based on the MediaPipe Hands solution, a highly optimized neural network. The algorithm processes the landmarks provided by MediaPipe using morphological and logical operators to obtain the masks that allow dynamic updating of the skin color model. Different experiments were carried out comparing the influence of the color space on skin segmentation, with the CIELab color space chosen as the best option. An average intersection over union of 0.869 was achieved on the demanding Ego2Hands dataset running at 90 frames per second on a conventional computer without any hardware acceleration. Finally, the proposed seg- mentation procedure was implemented in an augmented reality application to add hand occlusion for improved user immer- sion. An open-source implementation of the algorithm is publicly available at https://github.com/itap-robotica-medica/light weight-hand-segmentation.Ministerio de Ciencia e Innovación (under Grant Agreement No. RTC2019-007350-1)Publicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCL

HGR-Net: A Fusion Network for Hand Gesture Segmentation and Recognition

We propose a two-stage convolutional neural network (CNN) architecture for robust recognition of hand gestures, called HGR-Net, where the first stage performs accurate semantic segmentation to determine hand regions, and the second stage identifies the gesture. The segmentation stage architecture is based on the combination of fully convolutional residual network and atrous spatial pyramid pooling. Although the segmentation sub-network is trained without depth information, it is particularly robust against challenges such as illumination variations and complex backgrounds. The recognition stage deploys a two-stream CNN, which fuses the information from the red-green-blue and segmented images by combining their deep representations in a fully connected layer before classification. Extensive experiments on public datasets show that our architecture achieves almost as good as state-of-the-art performance in segmentation and recognition of static hand gestures, at a fraction of training time, run time, and model size. Our method can operate at an average of 23 ms per frame