2,001 research outputs found
CNN Based Posture-Free Hand Detection
Although many studies suggest high performance hand detection methods, those
methods are likely to be overfitting. Fortunately, the Convolution Neural
Network (CNN) based approach provides a better way that is less sensitive to
translation and hand poses. However the CNN approach is complex and can
increase computational time, which at the end reduce its effectiveness on a
system where the speed is essential.In this study we propose a shallow CNN
network which is fast, and insensitive to translation and hand poses. It is
tested on two different domains of hand datasets, and performs in relatively
comparable performance and faster than the other state-of-the-art hand
CNN-based hand detection method. Our evaluation shows that the proposed shallow
CNN network performs at 93.9% accuracy and reaches much faster speed than its
competitors.Comment: 4 pages, 5 figures, in The 10th International Conference on
Information Technology and Electrical Engineering 2018, ISBN:
978-1-5386-4739-
Egocentric Hand Detection Via Dynamic Region Growing
Egocentric videos, which mainly record the activities carried out by the
users of the wearable cameras, have drawn much research attentions in recent
years. Due to its lengthy content, a large number of ego-related applications
have been developed to abstract the captured videos. As the users are
accustomed to interacting with the target objects using their own hands while
their hands usually appear within their visual fields during the interaction,
an egocentric hand detection step is involved in tasks like gesture
recognition, action recognition and social interaction understanding. In this
work, we propose a dynamic region growing approach for hand region detection in
egocentric videos, by jointly considering hand-related motion and egocentric
cues. We first determine seed regions that most likely belong to the hand, by
analyzing the motion patterns across successive frames. The hand regions can
then be located by extending from the seed regions, according to the scores
computed for the adjacent superpixels. These scores are derived from four
egocentric cues: contrast, location, position consistency and appearance
continuity. We discuss how to apply the proposed method in real-life scenarios,
where multiple hands irregularly appear and disappear from the videos.
Experimental results on public datasets show that the proposed method achieves
superior performance compared with the state-of-the-art methods, especially in
complicated scenarios
Contextual Attention for Hand Detection in the Wild
We present Hand-CNN, a novel convolutional network architecture for detecting hand masks and predicting hand orientations in unconstrained images. Hand-CNN extends MaskRCNN with a novel attention mechanism to incorporate contextual cues in the detection process. This attention mechanism can be implemented as an efficient network module that captures non-local dependencies between features. This network module can be inserted at different stages of an object detection network, and the entire detector can be trained end-to-end. We also introduce large-scale annotated hand datasets containing hands in unconstrained images for training and evaluation. We show that Hand-CNN outperforms existing methods on the newly collected datasets and the publicly available PASCAL VOC human layout dataset. Data and code: https://www3.cs.stonybrook.edu/~cvl/projects/hand_det_attention
Contextual Attention for Hand Detection in the Wild
We present Hand-CNN, a novel convolutional network architecture for detecting
hand masks and predicting hand orientations in unconstrained images. Hand-CNN
extends MaskRCNN with a novel attention mechanism to incorporate contextual
cues in the detection process. This attention mechanism can be implemented as
an efficient network module that captures non-local dependencies between
features. This network module can be inserted at different stages of an object
detection network, and the entire detector can be trained end-to-end.
We also introduce a large-scale annotated hand dataset containing hands in
unconstrained images for training and evaluation. We show that Hand-CNN
outperforms existing methods on several datasets, including our hand detection
benchmark and the publicly available PASCAL VOC human layout challenge. We also
conduct ablation studies on hand detection to show the effectiveness of the
proposed contextual attention module.Comment: 9 pages, 9 figure
Detecting Hands in Egocentric Videos: Towards Action Recognition
Recently, there has been a growing interest in analyzing human daily
activities from data collected by wearable cameras. Since the hands are
involved in a vast set of daily tasks, detecting hands in egocentric images is
an important step towards the recognition of a variety of egocentric actions.
However, besides extreme illumination changes in egocentric images, hand
detection is not a trivial task because of the intrinsic large variability of
hand appearance. We propose a hand detector that exploits skin modeling for
fast hand proposal generation and Convolutional Neural Networks for hand
recognition. We tested our method on UNIGE-HANDS dataset and we showed that the
proposed approach achieves competitive hand detection results
Hand Detection using HSV Model
Natural Human Computer Interaction (HCI) is the demand of today’s technology oriented world. Detecting and tracking of face and hands are important for gesture recognition. Skin detection is a very popular and useful technique for detecting and tracking human-body parts. It has been much attention mainly because of its vast range of applications such as, face detection and tracking, naked people detection, hand detection and tracking, people retrieval in databases and Internet, etc. Many models and algorithms are being used for detection of face, hand and its gesture. Hand detection using model or classification is to build a decision rule that will discriminate between skin and non-skin pixels. Identifying skin color pixels involves finding the range of values for which most skin pixels would fall in a given color space. All external factors will be eliminated to detect the hand and its color in the image in complex background. Keywords: image segmentation, hand detection, hci, computer vision, RGB, HS
Stereoscopic hand-detection system based on FPGA
Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores (Major de Telecomunicações). Faculdade de Engenharia. Universidade do Porto. 200
Hand Detection and Body Language Recognition Using YOLO
Neural Networks play an important role in real-time object detection. Several types of networks are being developed in order to perform such detections at a faster pace. One such neural network that can prove useful is the YOLO network. Built to perform real-time detection, YOLO offers great speeds for simple detections. The goal of our research is to see how YOLO would work with body language. Would it be fast enough? And how accurate would it be? Compared to other forms of object detection, body-language detection is more vague. There are several factors to be accounted for. This is why we first begin by talking about hand recognition and gesture recognition, and then move onto body language. This research aims at understanding how YOLO would perform when subject to several tests by using its implementations, building datasets, training and testing the models to see whether it is successful in detecting hand gestures and body language
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