3 research outputs found
Efficient Hand Articulations Tracking using Adaptive Hand Model and Depth map
Real-time hand articulations tracking is important for many applications such
as interacting with virtual / augmented reality devices or tablets. However,
most of existing algorithms highly rely on expensive and high power-consuming
GPUs to achieve real-time processing. Consequently, these systems are
inappropriate for mobile and wearable devices. In this paper, we propose an
efficient hand tracking system which does not require high performance GPUs. In
our system, we track hand articulations by minimizing discrepancy between depth
map from sensor and computer-generated hand model. We also initialize hand pose
at each frame using finger detection and classification. Our contributions are:
(a) propose adaptive hand model to consider different hand shapes of users
without generating personalized hand model; (b) improve the highly efficient
frame initialization for robust tracking and automatic initialization; (c)
propose hierarchical random sampling of pixels from each depth map to improve
tracking accuracy while limiting required computations. To the best of our
knowledge, it is the first system that achieves both automatic hand model
adjustment and real-time tracking without using GPUs.Comment: Advances in Visual Computing: 11th International Symposium on Visual
Computing (ISVC'15
DenseAttentionSeg: Segment Hands from Interacted Objects Using Depth Input
We propose a real-time DNN-based technique to segment hand and object of
interacting motions from depth inputs. Our model is called DenseAttentionSeg,
which contains a dense attention mechanism to fuse information in different
scales and improves the results quality with skip-connections. Besides, we
introduce a contour loss in model training, which helps to generate accurate
hand and object boundaries. Finally, we propose and release our InterSegHands
dataset, a fine-scale hand segmentation dataset containing about 52k depth maps
of hand-object interactions. Our experiments evaluate the effectiveness of our
techniques and datasets, and indicate that our method outperforms the current
state-of-the-art deep segmentation methods on interaction segmentation
Random Forest with Learned Representations for Semantic Segmentation
In this work, we present a random forest framework that learns the weights,
shapes, and sparsities of feature representations for real-time semantic
segmentation. Typical filters (kernels) have predetermined shapes and
sparsities and learn only weights. A few feature extraction methods fix weights
and learn only shapes and sparsities. These predetermined constraints restrict
learning and extracting optimal features. To overcome this limitation, we
propose an unconstrained representation that is able to extract optimal
features by learning weights, shapes, and sparsities. We, then, present the
random forest framework that learns the flexible filters using an iterative
optimization algorithm and segments input images using the learned
representations. We demonstrate the effectiveness of the proposed method using
a hand segmentation dataset for hand-object interaction and using two semantic
segmentation datasets. The results show that the proposed method achieves
real-time semantic segmentation using limited computational and memory
resources