Robust recognition of hand gestures in real-world applications is still an unaccomplished goal due to many remaining challenges, such as cluttered backgrounds and unconstrained environmental factors. In most existing methods in this field, hand segmentation is a critical step to reduce redundant information in the scene background, in preparation for the hand gesture recognition stage. Therefore, we propose a new two-stage convolutional neural network (CNN) architecture, called HGR-Net, where the first stage performs accurate pixel-level semantic segmentation to determine the hand regions, and the second stage identifies the hand 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 significantly robust against challenges such as illumination variations and complex backgrounds. The recognition stage deploys a two-stream CNN which fuses the information from the RGB and segmented images by combining their deep representations in a new fully connected layer before classification. Extensive experiments on public hand gesture datasets show that our deep 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.
Hand Segmentation - Segmentation of hands plays an important role in many computer vision applications, such as hand tracking @cite_53 , hand pose estimation @cite_49 and gesture recognition @cite_54 . In recent years, skin segmentation has been widely used by several approaches for hand segmentation @cite_27 @cite_26 @cite_19 . Among such works color-based methods have been popular with many applying threshold-based techniques on different color spaces to achieve the best skin segmentation performance @cite_37 @cite_44 @cite_29 .
@cite_37 @cite_26 @cite_53 @cite_54 @cite_29 @cite_44 @cite_19 @cite_27 @cite_49
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We present a realtime hand tracking system using a depth sensor. It tracks a fully articulated hand under large viewpoints in realtime (25 FPS on a desktop without using a GPU) and with high accuracy (error below 10 mm). To our knowledge, it is the first system that achieves such robustness, accuracy, and speed simultaneously, as verified on challenging real data. Our system is made of several novel techniques. We model a hand simply using a number of spheres and define a fast cost function. Those are critical for realtime performance. We propose a hybrid method that combines gradient based and stochastic optimization methods to achieve fast convergence and good accuracy. We present new finger detection and hand initialization methods that greatly enhance the robustness of tracking. This chapter presents an overview of existing methods for human skin detection and segmentation. First of all, the skin color modeling schemes are outlined, and their limitations are discussed based on the presented experimental study. Then, we explain the techniques which were reported helpful in improving the efficacy of color-based classification, namely (1) textural features extraction, (2) model adaptation schemes, and (3) spatial analysis of the skin blobs. The chapter presents meaningful qualitative and quantitative results obtained during our study, which demonstrate the benefits of exploiting particular techniques for improving the skin detection outcome. This work investigates a gesture segmentation and recognition scheme that employs a random forest classification model. Our method trains a random forest model to recognize gestures from a given vocabulary, as presented in a training dataset of video plus 3D body joint locations, as well as out-of-vocabulary (non-gesture) instances. Given an input video stream, our trained model is applied to candidate gestures using sliding windows at multiple temporal scales. The class label with the highest classifier confidence is selected, and its corresponding scale is used to determine the segmentation boundaries in time. We evaluated our formulation in segmenting and recognizing gestures from two different benchmark datasets: the NATOPS dataset of 9,600 gesture instances from a vocabulary of 24 aircraft handling signals, and the ChaLearn dataset of 7,754 gesture instances from a vocabulary of 20 Italian communication gestures. The performance of our method compares favorably with state-of-the-art methods that employ Hidden Markov Models or Hidden Conditional Random Fields on the NATOPS dataset. Skin segmentation is the cornerstone of many applications such as gesture recognition, face detection, and objectionable image filtering. In this paper, we attempt to address the skin segmentation problem for gesture recognition. Initially, given a gesture video sequence, a generic skin model is applied to the first couple of frames to automatically collect the training data. Then, an SVM classifier based on active learning is used to identify the skin pixels. Finally, the results are improved by incorporating region segmentation. The proposed algorithm is fully automatic and adaptive to different signers. We have tested our approach on the ECHO database. Comparing with other existing algorithms, our method could achieve better performance. We present a novel method for real-time continuous pose recovery of markerless complex articulable objects from a single depth image. Our method consists of the following stages: a randomized decision forest classifier for image segmentation, a robust method for labeled dataset generation, a convolutional network for dense feature extraction, and finally an inverse kinematics stage for stable real-time pose recovery. As one possible application of this pipeline, we show state-of-the-art results for real-time puppeteering of a skinned hand-model.