Semantic Boundary Detection with Reinforcement Learning For Continuous Sign Language Recognition
Product Description
Technology: Machine Learning Tool: Matlab Matlab R2018a
Objective
The main aim of this project is used to recognize the sign language verification by using deep learning techniques.
ABSTRACT
In this paper, currently, there are tens of millions of people around communication way, sign language conveys semantic meaning by hand movement, gesture appearance, facial expression, etc. Image-based sign language recognition (SLR) is studied to automatically translate a sign image into ordered sign glosses, which may be further converted into natural language text or synthetic audio, to facilitate the communication between hearing-disabled people and hearing-normal people. With the huge potential social impact as well as the academic significance, video-based SLR has attracted increasing attention in the computer vision and multimedia fields. In this existing method is, a novel semantic boundary detection method based on reinforcement learning for accurate continues SLR.
Then, we formulate the semantic boundary detection as a reinforcement learning problem. In this paper, we propose a novel deep architecture for weakly supervised continuous SLR. Our framework consists of a weakly supervised learning component with a multi-scale perception module to learn discriminative image representation, and a reinforcement learning component to detect the semantic boundaries for further representation refinement, and enhance the performance.
PROPOSED METHOD
Propose a stacked temporal convolution module to capture short-term temporal transition on local adjacent video clips for continuous SLR. For spatial-temporal feature extraction in continuous SLR, many works, adopt both 2D-CNN and temporal convolution in their networks, which effectively promotes the feature discrimination. There are two stages in our method. First, we proposed a multi scale perception strategy to facilitate image representation by weakly supervised learning (SL). Second, we learn an agent called SBD-RL to detect the semantic boundaries in videos by reinforcement learning (RL). The two stages are trained separately. The output of the first stage is used as the input of the second stage. In this section, we expound the two stages separately.
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The Delivery time for Hardware Mini projects is 7-8 working days.