scholarly journals HD: Efficient Hand Detection and Tracking

2016 ◽  
Author(s):  
Joanna Isabelle Olszewska ◽  
Cleveland Rouge ◽  
Sohil Shaikh
Keyword(s):  
Hand Detection ◽  
Detection And Tracking

Detection of Hands for Hand-Controlled Skyfall Game in Real Time Using CNN

2020 ◽  
Vol 10 (2) ◽  
pp. 15-25
Author(s):  
Neha B. ◽  
Naveen V. ◽  
Angelin Gladston
Keyword(s):  
Real Time ◽  
Detection Methods ◽  
Input Device ◽  
Single Shot ◽  
Hand Detection ◽  
Web Based ◽  
Fast Detection ◽  
Detection And Tracking ◽  
Average Accuracy ◽  
Direct Use

With human-computer interaction technology evolving, direct use of the hand as an input device is of wide attraction. Recently, object detection methods using CNN models have significantly improved the accuracy of hand detection. This paper focuses on creating a hand-controlled web-based skyfall game by building a real time hand detection using CNN-based technique. A CNN network, which uses a MobileNet as the feature extractor along with the single shot detector framework, is used to achieve a robust and fast detection of hand location and tracking. Along with detection and tracking of hand, skyfall game has been designed to play using hand in real time with tensor flow framework. This way of designing the game where hand is used as input to control the paddle of skyfall game improved the player interaction and interest towards playing the game. This model of CNN network used egohands dataset for detecting and tracking the hands in real time and produced an average accuracy of 0.9 for open hands and 0.6 for closed hands which in turn improved player and game interactions.

Template-Based Hand Detection and Tracking

2005 ◽  
pp. 114-125 ◽  
Author(s):  
R. Cipolla ◽  
B. Stenger ◽  
A. Thayananthan ◽  
P. H. S. Torr
Keyword(s):  
Hand Detection ◽  
Detection And Tracking

Hand Detection and Tracking Using the Skeleton of the Blob for Medical Rehabilitation Applications

2012 ◽  
pp. 130-137 ◽  
Author(s):  
Pedro Gil-Jiménez ◽  
Beatriz Losilla-López ◽  
Rafael Torres-Cueco ◽  
Aurélio Campilho ◽  
Roberto López-Sastre
Keyword(s):  
Medical Rehabilitation ◽  
Hand Detection ◽  
Detection And Tracking

scholarly journals Real-Time Depth-Based Hand Detection and Tracking

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Sung-Il Joo ◽  
Sun-Hee Weon ◽  
Hyung-Il Choi
Keyword(s):  
Real Time ◽  
Mean Shift ◽  
Region Growing ◽  
Tracking Accuracy ◽  
Hand Detection ◽  
Seed Point ◽  
Mean Shift Algorithm ◽  
Detection And Tracking ◽  
Cascade Structure ◽  
Hand Region

This paper illustrates the hand detection and tracking method that operates in real time on depth data. To detect a hand region, we propose the classifier that combines a boosting and a cascade structure. The classifier uses the features of depth-difference at the stage of detection as well as learning. The features of each candidate segment are to be computed by subtracting the averages of depth values of subblocks from the central depth value of the segment. The features are selectively employed according to their discriminating power when constructing the classifier. To predict a hand region in a successive frame, a seed point in the next frame is to be determined. Starting from the seed point, a region growing scheme is applied to obtain a hand region. To determine the central point of a hand, we propose the so-called Depth Adaptive Mean Shift algorithm. DAM-Shift is a variant of CAM-Shift (Bradski, 1998), where the size of the search disk varies according to the depth of a hand. We have evaluated the proposed hand detection and tracking algorithm by comparing it against the existing AdaBoost (Friedman et al., 2000) qualitatively and quantitatively. We have analyzed the tracking accuracy through performance tests in various situations.

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