Two Hand Tracking Using Colour Statistical Model with the K-means Embedded Particle Filter for Hand Gesture Recognition

2008 ◽  
Author(s):  
Surachai Ongkittikul ◽  
Stewart Worrall ◽  
Ahmet Kondoz
Keyword(s):  
Statistical Model ◽  
Particle Filter ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Hand Tracking ◽  
Hand Gesture

Dynamic Hand Gesture Recognition Using Kinematic Features Based on Dynamic Time Warping

2012 ◽  
Vol 235 ◽  
pp. 68-73
Author(s):  
Hai Bo Pang ◽  
You Dong Ding
Keyword(s):  
Gesture Recognition ◽  
Dynamic Time Warping ◽  
Hand Gesture Recognition ◽  
Hand Tracking ◽  
Hand Gesture ◽  
Direction Vector ◽  
Time Warping ◽  
Motion Direction ◽  
Hand Motion ◽  
Dynamic Time

Hand gesture provides an attractive alternative to cumbersome interface devices for human computer interface. Many hand gesture recognition methods using visual analysis have been proposed. In our research, we exploit multiple cues including divergence features, vorticity features and hand motion direction vector. Divergence and vorticity are derived from the optical flow for hand gesture recognition in videos. Then these features are computed by principal component analysis method. The hand tracking algorithm finds the hand centroids for every frame, computes hand motion direction vector. At last, we introduced dynamic time warping method to verify the robustness of our features. Those experimental results demonstrate that the proposed approach yields a satisfactory recognition rate.

scholarly journals DBSCAN for Hand Tracking and Gesture Recognition

2020 ◽  
Vol 5 (2) ◽  
pp. 168
Author(s):  
Wisnu Aditya ◽  
Herman Tolle ◽  
Timothy K Shih
Keyword(s):  
Gesture Recognition ◽  
Clustering Algorithm ◽  
Hand Gesture Recognition ◽  
Computational Time ◽  
Hand Tracking ◽  
Hand Gesture ◽  
Depth Data ◽  
Dbscan Clustering ◽  
Hand Segmentation ◽  
Speed Up

Hand segmentation and tracking are important issues for hand-gesture recognition. Using depth data, it can speed up the segmentation process because we can delete unnecessary data like the background of the image easily. In this research, we modify DBSCAN clustering algorithm to make it faster and suitable for our system. This method is used in both hand tracking and hand gesture recognition. The results show that our method performs well in this system. The proposed method can outperform the original DBSCAN and the other clustering method in terms of computational time.

scholarly journals Hand Tracking and Hand Gesture Recognition for Human Computer Interaction

2011 ◽  
Vol 14 (2) ◽  
pp. 182-193 ◽  
Author(s):  
Yu Bai ◽  
Sang-Yun Park ◽  
Yun-Sik Kim ◽  
In-Gab Jeong ◽  
Soo-Yol Ok ◽  
...  
Keyword(s):  
Human Computer Interaction ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Hand Tracking ◽  
Hand Gesture ◽  
Computer Interaction

scholarly journals Hand Gesture Recognition Using 3-D Depth Perception

2019 ◽  
Vol 9 (1S) ◽  
pp. 116-120
Keyword(s):  
Gesture Recognition ◽  
Depth Perception ◽  
Hand Gesture Recognition ◽  
Sensor Technology ◽  
Hand Tracking ◽  
Hand Gesture ◽  
Human Computer Interactions ◽  
The Past ◽  
Computer Interactions

The paper provides information on hand gesture recognition through its various methods but primarily focused on 3D depth-perceiving sensor technology for hand tracking and gesture recognition. Hand gesture recognition provides new opportunities for human-computer interactions (HCI) and over the past decade has emerged in various fields and technologies. The paper lures an likeness amongst the present technologies in the market and the workings behind their operations. It differentiates between the old and newer mechanics employed in the products. Furthermore, the paper discusses the existing uses of gesture recognition with their limitations. It provides proposed ideas in various fields, their use and future developmental paths.

Connectionist Temporal Classification Model for Dynamic Hand Gesture Recognition using RGB and Optical flow Data

2020 ◽  
Vol 17 (4) ◽  
pp. 497-506
Author(s):  
Sunil Patel ◽  
Ramji Makwana
Keyword(s):  
Neural Network ◽  
Optical Flow ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Classification Model ◽  
Hand Gesture ◽  
Flow Data ◽  
Dynamic Hand Gesture Recognition ◽  
Connectionist Temporal Classification

Automatic classification of dynamic hand gesture is challenging due to the large diversity in a different class of gesture, Low resolution, and it is performed by finger. Due to a number of challenges many researchers focus on this area. Recently deep neural network can be used for implicit feature extraction and Soft Max layer is used for classification. In this paper, we propose a method based on a two-dimensional convolutional neural network that performs detection and classification of hand gesture simultaneously from multimodal Red, Green, Blue, Depth (RGBD) and Optical flow Data and passes this feature to Long-Short Term Memory (LSTM) recurrent network for frame-to-frame probability generation with Connectionist Temporal Classification (CTC) network for loss calculation. We have calculated an optical flow from Red, Green, Blue (RGB) data for getting proper motion information present in the video. CTC model is used to efficiently evaluate all possible alignment of hand gesture via dynamic programming and check consistency via frame-to-frame for the visual similarity of hand gesture in the unsegmented input stream. CTC network finds the most probable sequence of a frame for a class of gesture. The frame with the highest probability value is selected from the CTC network by max decoding. This entire CTC network is trained end-to-end with calculating CTC loss for recognition of the gesture. We have used challenging Vision for Intelligent Vehicles and Applications (VIVA) dataset for dynamic hand gesture recognition captured with RGB and Depth data. On this VIVA dataset, our proposed hand gesture recognition technique outperforms competing state-of-the-art algorithms and gets an accuracy of 86%

Feature Fusion Based Hand Gesture Recognition Method for Automotive Interfaces

2020 ◽  
Vol 29 (6) ◽  
pp. 1153-1164
Author(s):  
Qianyi Xu ◽  
Guihe Qin ◽  
Minghui Sun ◽  
Jie Yan ◽  
Huiming Jiang ◽  
...  
Keyword(s):  
Gesture Recognition ◽  
Feature Fusion ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Recognition Method
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