Hand detection is essential for many hand related tasks, e.g. parsing hand pose, understanding gesture, which are extremely useful for robotics and human-computer interaction. However, hand detection in uncontrolled environments is challenging due to the flexibility of wrist joint and cluttered background. We propose a deep learning based approach which detects hands and calibrates in-plane rotation under supervision at the same time. To guarantee the recall, we propose a context aware proposal generation algorithm which significantly outperforms the selective search. We then design a convolutional neural network(CNN) which handles object rotation explicitly to jointly solve the object detection and rotation estimation tasks. Experiments show that our method achieves better results than state-of-the-art detection models on widely-used benchmarks such as Oxford and Egohands database. We further show that rotation estimation and classification can mutually benefit each other.
These methods build a skin model with either Gaussian mixture models @cite_28 , or using prior knowledge of skin color from face detection @cite_4 . However, these methods often fail to apply to hand detection in general conditions due to the fact that complex illuminations often lead to large variations in skin color and make the skin color modelling problem challenging.
@cite_28 @cite_4
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This paper introduces a new method for skin regions segmentation which consists in spatial analysis of skin probability maps obtained using pixel-wise detectors. There are a number of methods which use various techniques of skin color modeling to classify every individual pixel or transform input color images into skin probability maps, but their performance is limited due to high variance and low specificity of the skin color. Detection precision can be enhanced based on spatial analysis of skin pixels, however this direction has been little explored so far. Our contribution lies in using the distance transform for propagating the “skinness” across the image in a combined domain of luminance, hue and skin probability. In the paper we explain theoretical advantages of the proposed method over alternative skin detectors that also perform spatial analysis. Finally, we present results of an extensive experimental study which clearly indicate high competitiveness of the proposed method and its relevance to gesture recognition. The skin probability maps are refined using discriminative features (DSPFs).Spatial analysis of skin pixels is more successful when performed in DSPF skin maps.The textural features are extracted from skin maps rather than from the grayscale. In this paper we propose a new method for skin detection in color images which consists in spatial analysis using the introduced texture-based discriminative skin-presence features. Color-based skin detection has been widely explored and many skin color modeling techniques were developed so far. However, efficacy of the pixel-wise classification is limited due to an overlap between the skin and non-skin pixels reported in many color spaces. To increase the discriminating power of the skin classification schemes, textural and spatial features are often exploited for skin modeling. Our contribution lies in using the proposed discriminative feature space as a domain for spatial analysis of skin pixels. Contrary to existing approaches, we extract the textural features from the skin probability maps rather than from the luminance channel. Presented experimental study confirms that the proposed method outperforms alternative skin detection techniques, which also involve analysis of textural and spatial features. Skin detection plays an important role in a wide range of image processing applications ranging from face detection, face tracking, gesture analysis, content-based image retrieval systems and to various human computer interaction domains. Recently, skin detection methodologies based on skin-color information as a cue has gained much attention as skin-color provides computationally effective yet, robust information against rotations, scaling and partial occlusions. Skin detection using color information can be a challenging task as the skin appearance in images is affected by various factors such as illumination, background, camera characteristics, and ethnicity. Numerous techniques are presented in literature for skin detection using color. In this paper, we provide a critical up-to-date review of the various skin modeling and classification strategies based on color information in the visual spectrum. The review is divided into three different categories: first, we present the various color spaces used for skin modeling and detection. Second, we present different skin modeling and classification approaches. However, many of these works are limited in performance due to real-world conditions such as illumination and viewing conditions. To cope up with the rapidly changing illumination conditions, illumination adaptation techniques are applied along with skin-color detection. Third, we present various approaches that use skin-color constancy and dynamic adaptation techniques to improve the skin detection performance in dynamically changing illumination and environmental conditions. Wherever available, we also indicate the various factors under which the skin detection techniques perform well. 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.