Two streams Recurrent Neural Networks for Large-Scale Continuous Gesture Recognition
In this paper, we tackle the continuous gesture recognition problem with a two streams Recurrent Neural Networks (2S-RNN) for the RGB-D data input. In our framework, the spotting-recognition strategy is used, that means the continuous gestures are first segmented into separated gestures, and then each isolated gesture is recognized by using the 2S-RNN. Concretely, the gesture segmentation is based on the accurate hand positions provided by the hand detector trained from Faster R-CNN. While in the recognition module, 2S-RNN is designed to efficiently fuse multi-modal features, i.e. the RGB and depth channels. The experimental results on both the validation and test sets of the Continuous Gesture Dataset (ConGD) have shown promising performance of the proposed framework. We ranked 1st in the ChaLearn LAP Large-scale Continuous Gesture Recognition Challenge with the mean Jaccard Index of 0.286915.