A Human Body Analysis System

Girondel, Vincent; Bonnaud, Laurent; Caplier, Alice

doi:10.1155/asp/2006/61927

Cited by 7 publications

(4 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In their papers [16,17], Dardas et al suggested a thresholding technique for fragmenting hands in the hue, saturation, and value (HSV) color space after extracting other skin regions, such as the face, from the source image. Girondel and colleagues [18] experimented with a variety of color spaces and discovered that the Cb and Cr channels in the YCbCr color space performed well in the skin detection task. Sigal et al [10] proposed the Gaussian mixture model, which performed admirably under a variety of lighting conditions.…”

Section: Hand Detection and Recognitionmentioning

confidence: 99%

Combination of Deep Cross-Stage Partial Network and Spatial Pyramid Pooling for Automatic Hand Detection

Dewi

Christanto

2022

BDCC

View full text Add to dashboard Cite

The human hand is involved in many computer vision tasks, such as hand posture estimation, hand movement identification, human activity analysis, and other similar tasks, in which hand detection is an important preprocessing step. It is still difficult to correctly recognize some hands in a cluttered environment because of the complex display variations of agile human hands and the fact that they have a wide range of motion. In this study, we provide a brief assessment of CNN-based object identification algorithms, specifically Densenet Yolo V2, Densenet Yolo V2 CSP, Densenet Yolo V2 CSP SPP, Resnet 50 Yolo V2, Resnet 50 CSP, Resnet 50 CSP SPP, Yolo V4 SPP, Yolo V4 CSP SPP, and Yolo V5. The advantages of CSP and SPP are thoroughly examined and described in detail in each algorithm. We show in our experiments that Yolo V4 CSP SPP provides the best level of precision available. The experimental results show that the CSP and SPP layers help improve the accuracy of CNN model testing performance. Our model leverages the advantages of CSP and SPP. Our proposed method Yolo V4 CSP SPP outperformed previous research results by an average of 8.88%, with an improvement from 87.6% to 96.48%.

show abstract

Section: Hand Detection and Recognitionmentioning

confidence: 99%

Combination of Deep Cross-Stage Partial Network and Spatial Pyramid Pooling for Automatic Hand Detection

Dewi

Christanto

2022

BDCC

View full text Add to dashboard Cite

show abstract

“…are employed to detect the appearance of targeted objects. Since the diversity of skin colors and changes in illumination can affect the detection accuracy, Girondel et al [ 2 ] found that

and

color channels are more suitable for the skin detection task. Sigal et al [ 3 ] proposed the Gaussian mixture model that performed quite well under varying illumination conditions.…”

Section: Related Work Of Hand Detectionmentioning

confidence: 99%

Egocentric-View Fingertip Detection for Air Writing Based on Convolutional Neural Networks

Chen

Huang

Syu

et al. 2021

Sensors

View full text Add to dashboard Cite

This research investigated real-time fingertip detection in frames captured from the increasingly popular wearable device, smart glasses. The egocentric-view fingertip detection and character recognition can be used to create a novel way of inputting texts. We first employed Unity3D to build a synthetic dataset with pointing gestures from the first-person perspective. The obvious benefits of using synthetic data are that they eliminate the need for time-consuming and error-prone manual labeling and they provide a large and high-quality dataset for a wide range of purposes. Following that, a modified Mask Regional Convolutional Neural Network (Mask R-CNN) is proposed, consisting of a region-based CNN for finger detection and a three-layer CNN for fingertip location. The process can be completed in 25 ms per frame for 640×480 RGB images, with an average error of 8.3 pixels. The speed is high enough to enable real-time “air-writing”, where users are able to write characters in the air to input texts or commands while wearing smart glasses. The characters can be recognized by a ResNet-based CNN from the fingertip trajectories. Experimental results demonstrate the feasibility of this novel methodology.

show abstract

“…These hand detection algorithms occupy less computational resources. In order to solve the skin color diversity problem caused by human races and illumination change, Girondel et al [32] tried several color spaces and found that Cb and Cr channels in YCbCr color space worked well in skin detection task. Sigal et al [10] proposed the Gaussian mixture model that performed well in different illumination conditions.…”

Section: Related Workmentioning

confidence: 99%

Accurate Hand Detection from Single-Color Images by Reconstructing Hand Appearances

Cai

et al. 2019

Sensors

View full text Add to dashboard Cite

Hand detection is a crucial pre-processing procedure for many human hand related computer vision tasks, such as hand pose estimation, hand gesture recognition, human activity analysis, and so on. However, reliably detecting multiple hands from cluttering scenes remains to be a challenging task because of complex appearance diversities of dexterous human hands (e.g., different hand shapes, skin colors, illuminations, orientations, and scales, etc.) in color images. To tackle this problem, an accurate hand detection method is proposed to reliably detect multiple hands from a single color image using a hybrid detection/reconstruction convolutional neural networks (CNN) framework, in which regions of hands are detected and appearances of hands are reconstructed in parallel by sharing features extracted from a region proposal layer, and the proposed model is trained in an end-to-end manner. Furthermore, it is observed that the generative adversarial network (GAN) could further boost the detection performance by generating more realistic hand appearances. The experimental results show that the proposed approach outperforms the state-of-the-art on public challenging hand detection benchmarks.Sensors 2020, 20, 192 2 of 21 and hand-related applications in an unconstrained environment (complex background and the number of hands in an image is unknown) will be an important trend in the future. In this condition, hand detection in an unconstrained environment becomes a new bottleneck in the hand-related works. Thus, the high precision hand detection method will be a crucial step in the pipeline for hand-related applications in unconstrained environment. In this paper, we focus on the hand detection algorithm.Traditional hand detection methods primarily utilize low-level image features such as skin color [10] and shape [11,12] for hand region detection. Nowadays, convolutional neural networks (CNN) based detection approaches [13][14][15][16][17][18][19] are proved to be more robust and accurate [20][21][22] due to the discriminative deep features learned. However, compared to common objects, human hands are highly articulated, appearing in various orientations, scales, shapes, skin colors, and sometimes partial occlusions, therefore reliably detecting multiple human hands from unconstrained cluttering scenes remains to be a challenging problem.To tackle this problem, we propose an approach to accurately detect human hands from single color images by reconstructing the hand appearances. It can also be applied to video clips, as video clips can be considered as sequences of single images. The spirit of our approach is primarily oriented from multitask learning [23] which improves generalization of the network by learning tasks in parallel while using a shared representation. The quality of the hand detection task is closely related to the diversity of hand appearances in terms of hand shape, skin color, orientation, scale, and partial occlusion, etc. As a result, the shared information contained in the training signal of the ha...

show abstract

A Human Body Analysis System

Cited by 7 publications

References 52 publications

Combination of Deep Cross-Stage Partial Network and Spatial Pyramid Pooling for Automatic Hand Detection

Combination of Deep Cross-Stage Partial Network and Spatial Pyramid Pooling for Automatic Hand Detection

Egocentric-View Fingertip Detection for Air Writing Based on Convolutional Neural Networks

Accurate Hand Detection from Single-Color Images by Reconstructing Hand Appearances

Contact Info

Product

Resources

About