DenseAttentionSeg: Segment Hands from Interacted Objects Using Depth Input

Abstract

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.

References in corpus (13)

• Adam: A Method for Stochastic Optimization
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Neural Machine Translation by Jointly Learning to Align and Translate
• TensorFlow: A system for large-scale machine learning
• Rethinking Atrous Convolution for Semantic Image Segmentation
• Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
• Fully Convolutional Networks for Semantic Segmentation
• Real-time Hand Tracking under Occlusion from an Egocentric RGB-D Sensor
• Pyramid Attention Network for Semantic Segmentation
• Deep Image Retrieval: Learning global representations for image search
• ExFuse: Enhancing Feature Fusion for Semantic Segmentation
• Real-time Joint Tracking of a Hand Manipulating an Object from RGB-D Input
• GANerated Hands for Real-time 3D Hand Tracking from Monocular RGB