HD: Efficient Hand Detection and Tracking

Hand Detection and Tracking in Videos for Fine-Grained Action Recognition

Computer Vision - ACCV 2014 Workshops - Lecture Notes in Computer Science ◽

10.1007/978-3-319-16628-5_2 ◽

2015 ◽

pp. 19-34

Author(s):

Nga H. Do ◽

Keiji Yanai

Keyword(s):

Action Recognition ◽

Hand Detection ◽

Fine Grained ◽

Detection And Tracking

Hands on the wheel: A Dataset for Driver Hand Detection and Tracking

2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018) ◽

10.1109/fg.2018.00090 ◽

2018 ◽

Cited By ~ 5

Author(s):

Guido Borghi ◽

Elia Frigieri ◽

Roberto Vezzani ◽

Rita Cucchiara

Keyword(s):

Hand Detection ◽

Detection And Tracking ◽

Hands On

Posture independent model for hand detection and tracking

2013 6th International Conference on Human System Interactions (HSI) ◽

10.1109/hsi.2013.6577819 ◽

2013 ◽

Cited By ~ 1

Author(s):

Madhat Alsoos ◽

Ammar Joukhadar

Keyword(s):

Hand Detection ◽

Detection And Tracking ◽

Independent Model

Vision-based two hand detection and tracking

Proceedings of the 2nd International Conference on Interaction Sciences Information Technology, Culture and Human - ICIS '09 ◽

10.1145/1655925.1656154 ◽

2009 ◽

Cited By ~ 2

Author(s):

Jiajun Wen ◽

Yinwei Zhan

Keyword(s):

Hand Detection ◽

Detection And Tracking

Robust Hand Detection and Tracking Based on Monocular Vision

2014 Sixth International Conference on Intelligent Human-Machine Systems and Cybernetics ◽

10.1109/ihmsc.2014.135 ◽

2014 ◽

Cited By ~ 2

Author(s):

Hao Liang ◽

Yong Zhao ◽

Jiangyue Wei ◽

Dongbing Quan ◽

Ruzhong Cheng ◽

...

Keyword(s):

Monocular Vision ◽

Hand Detection ◽

Detection And Tracking

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

International Journal of Interactive Communication Systems and Technologies ◽

10.4018/ijicst.2020070102 ◽

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.

Hand detection and tracking using pixel value distribution model for multiple-camera-based gesture interactions

Proceedings. IEEE Workshop on Knowledge Media Networking ◽

10.1109/kmn.2002.1115159 ◽

2003 ◽

Cited By ~ 15

Author(s):

A. Utsumi ◽

N. Tetsutani ◽

S. Igi

Keyword(s):

Value Distribution ◽

Distribution Model ◽

Hand Detection ◽

Multiple Camera ◽

Detection And Tracking ◽

Pixel Value

Template-Based Hand Detection and Tracking

Advanced Studies in Biometrics - Lecture Notes in Computer Science ◽

10.1007/11493648_7 ◽

2005 ◽

pp. 114-125 ◽

Cited By ~ 1

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

Lecture Notes in Computer Science - Image Analysis and Recognition ◽

10.1007/978-3-642-31298-4_16 ◽

2012 ◽

pp. 130-137 ◽

Cited By ~ 2

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

Real-Time Depth-Based Hand Detection and Tracking

The Scientific World JOURNAL ◽

10.1155/2014/284827 ◽

2014 ◽

Vol 2014 ◽

pp. 1-17 ◽

Cited By ~ 6

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.