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In this paper, we propose a machine learning-based multi-stream framework to recognize American Sign Language (ASL) manual signs and nonmanual gestures (face and head movements) in real time from RGB-D videos. Our approach is based on 3D Convolutional Neural Networks (3D CNNs) by fusing the multi-modal features including hand gestures, facial expressions, and body poses from multiple channels (RGB, Depth, Motion, and Skeleton joints). To learn the overall temporal dynamics in a video, a proxy video is generated by selecting a subset of frames for each video which are then used to train the proposed 3D CNN model. We collected a new ASL dataset, ASL-100-RGBD, which contains 42 RGB-D videos captured by a Microsoft Kinect V2 camera. Each video consists of 100 ASL manual signs, along with RGB channel, Depth maps, Skeleton joints, Face features, and HD face. The dataset is fully annotated for each semantic region (i.e. the time duration of each sign that the human signer performs). Our proposed method achieves 92.88% accuracy for recognizing 100 ASL sign glosses in our newly collected ASL-100-RGBD dataset. The effectiveness of our framework for recognizing hand gestures from RGB-D videos is further demonstrated on a large-scale dataset, ChaLearn IsoGD, achieving the state-of-the-art results.
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First-Person View Hand Segmentation of Multi-Modal Hand Activity Video Dataset.
First-person-view videos of hands interacting with tools are widely used in the computer vision industry. However, creating a dataset with pixel-wise segmentation of hands is challenging since most videos are captured with fingertips occluded by the hand dorsum and grasped tools. Current methods often rely on manually segmenting hands to create annotations, which is inefficient and costly. To relieve this challenge, we create a method that utilizes thermal information of hands for efficient pixel-wise hand segmentation to create a multi-modal activity video dataset. Our method is not affected by fingertip and joint occlusions and does not require hand pose ground truth. We show our method to be 24 times faster than the traditional polygon labeling method while maintaining high quality. With the segmentation method, we propose a multi-modal hand activity video dataset with 790 sequences and 401,765 frames of "hands using tools" videos captured by thermal and RGB-D cameras with hand segmentation data. We analyze multiple models for hand segmentation performance and benchmark four segmentation networks. We show that our multi-modal dataset with fusing Long-Wave InfraRed (LWIR) and RGB-D frames achieves 5% better hand IoU performance than using RGB frames.
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- Award ID(s):
- 1839971
- PAR ID:
- 10297591
- Date Published:
- Journal Name:
- BMVC 2020
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Conference Paper:
- The DOI is not currently available.
