Abstract
In this paper, we propose a novel particle filter based probabilistic forced alignment approach for training spatiotemporal deep neural networks using weak border level annotations. The proposed method jointly learns to localize and recognize isolated instances in continuous streams. This is done by drawing training volumes from a prior distribution of likely regions and training a discriminative 3D-CNN from this data. The classifier is then used to calculate the posterior distribution by scoring the training examples and using this as the prior for the next sampling stage. We apply the proposed approach to the challenging task of large-scale user-independent continuous gesture recognition. We evaluate the performance on the popular ChaLearn 2016 Continuous Gesture Recognition (ConGD) dataset. Our method surpasses state-of-the-art results by obtaining 0:3646 and 0:3744 Mean Jaccard Index Score on the validation and test sets of ConGD, respectively. Furthermore, we participated in the ChaLearn 2017 Continuous Gesture Recognition Challenge and was ranked 3rd. It should be noted that our method is learner independent, it can be easily combined with other approaches.