Eedoo Inc

Yinchuan, China

Eedoo Inc

Yinchuan, China
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Lin S.,Wuhan University | Hu R.M.,Wuhan University | Xiao Y.L.,Eedoo Inc | Gong L.Y.,Eedoo Inc
Advanced Materials Research | Year: 2013

In this paper, we propose a novel real-time 3D hand gesture recognition algorithm based on depth information. We segment out the hand region from depth image and convert it to a point cloud. Then, 3D moment invariant features are computed at the point cloud. Finally, support vector machine (SVM) is employed to classify the shape of hand into different categories. We collect a benchmark dataset using Microsoft Kinect for Xbox and test the propose algorithm on it. Experimental results prove the robustness of our proposed algorithm. © (2013) Trans Tech Publications, Switzerland.


Song L.,Wuhan University | Hu R.M.,Wuhan University | Xiao Y.L.,Eedoo Inc | Gong L.Y.,Eedoo Inc
Advanced Materials Research | Year: 2013

In this paper, we propose a depth image based real-time 3D hand tracking method. Our method is based on the fact that human hand is an end point of human body. Therefore, we locate human hand by finding the end point from a predicted position of hand based on the hand position of the previous frame. We iteratively grow a region around the predicted position. The end point on the major axis of the region which stops moving with region growing is selected as the final position of human hand. Experiments on Microsoft Kinect for Xbox captured sequences show the effectiveness and efficiency of our proposed method. © (2013) Trans Tech Publications, Switzerland.


Chen M.,Huazhong University of Science and Technology | Gong L.,Eedoo Inc | Wang T.,Huazhong University of Science and Technology | Feng Q.,Huazhong University of Science and Technology
Multimedia Tools and Applications | Year: 2015

This paper presents a novel framework for human action recognition based on a newly proposed mid-level feature representation method named Lie Algebrized Guassians (LAG). As an action sequence can be treated as a 3D object in space-time space, we address the action recognition problem by recognizing 3D objects and characterize 3D objects by the probability distributions of local spatio-temporal features. First, for each video, we densely sample local spatio-temporal features (e.g. HOG3D) at multiple scales confined in bounding boxes of human body. Moreover, normalized spatial coordinates are appended to local descriptor in order to capture spatial position information. Then the distribution of local features in each video is modeled by a Gaussian Mixture Model (GMM). To estimate the parameters of video-specific GMMs, a global GMM is trained using all training data and video-specific GMMs are adapted from the global GMM. Then the LAG is adopted to vectorize those video-specific GMMs. Finally, linear SVM is employed for classification. Experimental results on the KTH and UCF Sports dataset show that our method achieves state-of-the-art performance. © 2013, Springer Science+Business Media New York.


Hu C.,Huazhong University of Science and Technology | Gong L.,Eedoo Inc | Wang T.,Huazhong University of Science and Technology | Feng Q.,Huazhong University of Science and Technology
Multimedia Tools and Applications | Year: 2015

Automatically and effectively estimating human ages via facial images has lots of practical applications, such as security surveillance, electronic customer relationship management and entertainment. Motivated by the fact that feature representation and recognition are two key problems in facial image based human age estimation, in this paper, we propose to employ a novel discriminative feature called Lie Algebrized Gaussians (LAG) for the representation of age images and design a two-stage approach for learning and predicting human ages. LAG is built on Gaussian Mixture Models (GMM) and is able to capture the aging manifold of the age image by preserving the Lie group manifold structure information embedded in the feature space. Given the LAG feature for each image, we estimate the human age using a two-stage approach in a coarse-to-fine fashion. In the first stage, an adaptive age group for each input image is obtained by selecting a number of neighboring age labels around the output of a global regressor. In the second stage, a local classifier is learned from the selected age classes to determine the final age of the input image. The effectiveness of our approach is evaluated on both FG-NET and MORPH benchmarks, extensive experimental results and comparisons with the state-of-the-art algorithms demonstrate the superiority of our approach for the human age estimation task. © 2013, Springer Science+Business Media New York.


Chen M.,Huazhong University of Science and Technology | Gong L.,Eedoo Inc | Wang T.,Huazhong University of Science and Technology | Liu F.,Huazhong University of Science and Technology | Feng Q.,Huazhong University of Science and Technology
Multimedia Tools and Applications | Year: 2015

We propose a novel approach to model spatio-temporal distribution of local features for action recognition in videos. The proposed approach is based on the Lie Algebrized Gaussians (LAG) which is a feature aggregation approach and yields high-dimensional video signature. In the framework of LAG, local features extracted from a video are aggregated to train a video-specific Gaussian Mixture Model (GMM). Then the video-specific GMM is encoded as a vector based on Lie group theory and this step is also referred to as GMM vectorization. As the video-specific GMM gives a soft partition of the feature space, for each cell of the feature space (i.e. each Gaussian component), we use a GMM to model the spatio-temporal locations of the local features assigned to the Gaussian component. The location GMMs are encoded as vectors just like the local feature GMM. We term those vectors of location GMMs spatio-temporal LAG (STLAG). In addition, although the LAG and the popular Fisher Vector (FV) are derived from distinct theory perspectives, we find that they are closely related. Hence the power and ℓ2 normalization proposed for the FV are also beneficial to the LAG. Experimental results show that STLAG is very effective to model spatio-temporal layout compared with other techniques such as spatio-temporal pyramid and feature augmentation. Using the state-of-the-art dense trajectory features, our approach achieves state-of-the-art performance on two challenging datasets: Hollywood2 and HMDB51. © 2015 Springer Science+Business Media New York


Hu C.,Huazhong University of Science and Technology | Gong L.,Eedoo Inc | Wang T.,Huazhong University of Science and Technology | Liu F.,Huazhong University of Science and Technology | Feng Q.,Huazhong University of Science and Technology
Multimedia Tools and Applications | Year: 2014

The accuracy of head pose estimation is significant for many computer vision applications such as face recognition, driver attention detection and human-computer interaction. Most appearance-based head pose estimation works typically extract the low-dimensional face appearance features in some statistic subspaces, where the subspaces represent the underlying geometry structure of the pose space. However, there is an open problem, namely, how to effectively represent appearance-based subspace face for the head pose estimation problem. To address the problem, this paper proposes a head pose estimation approach based on the Lie Algebrized Gaussians (LAG) feature to model the pose characteristic. LAG is built on Gaussian Mixture Models (GMM), which actually not only models the distribution of local appearance features, but also captures the Lie group manifold structure of the feature space. Moreover, to keep multi-resolution structure information, LAG is operated on many subregions of the image. As a result, these properties of LAG enable it to effectively model the structure of subspace face which can lead to powerful discriminative ability for head pose estimation. After representing subspace face using the LAG, we treat the head pose estimation as a classification problem. The within-class covariance normalization (WCCN) based Support Vector Machine (SVM) classifier is employed to achieve robust performance as WCCN could reduce the within-class variabilities of the same pose. Extensive experimental analysis and comparison with both traditional and state-of-the-art algorithms on two challenging benchmarks demonstrate the effectiveness of our approach. © 2013, Springer Science+Business Media New York.


Song L.,Hubei Engineering University | Hu R.M.,Hubei Engineering University | Zhang H.,Hubei Engineering University | Xiao Y.L.,Eedoo Inc. | Gong L.Y.,Huazhong University of Science and Technology
Advanced Materials Research | Year: 2013

In this paper, we describe an real-time algorithm to detect 3D hand gestures from depth images. Firstly, we detect moving regions by frame difference; then, regions are refined by removing small regions and boundary regions; finally, foremost region is selected and its trajectories are classified using an automatic state machine. Experiments on Microsoft Kinect for Xbox captured sequences show the effectiveness and efficiency of our system. © (2013) Trans Tech Publications, Switzerland.

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