Recurrent Neural Networks for Activity Recognition in Video Sequences


Recurrent Neural Networks for Activity Recognition in Video Sequences – In this paper, on the basis of the similarity between our results from the field of video signal processing, we propose an effective method for the detection of different forms of occlusion in videos based on the use of 3D facial pose estimation. Our approach is based on the use of the 3D facial pose estimation algorithm to generate a fully 2D representation of the scene. This representation is used for 3D facial pose estimation. Using the facial pose estimation algorithm we identify occlusions in videos consisting of multiple occlusions. We use a large number of images and a large number of frames and demonstrate the effectiveness of our method with a variety of applications including 3D face recognition, 3D motion segmentation, and 3D motion labeling.

We propose a novel deep learning approach to image classification. The training of deep generative models for image classification is carried out by using local feature extraction and deep neural networks (DNNs). We trained deep generative models using a dictionary-based representation of the image, and then trained deep generative models using a local dictionary representation for each image segment. We further evaluated an image classification method which uses a dictionary-based representation and local feature extraction to train a deep generative model using both locally discriminative and discriminative features. The proposed approach is compared with other methods based on a discriminative model and learned feature extraction.

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Recurrent Neural Networks for Activity Recognition in Video Sequences

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  • Visual-Inertial Character Recognition with Learned Deep Convolutional Sparse Representation

    Machine Learning and Deep LearningWe propose a novel deep learning approach to image classification. The training of deep generative models for image classification is carried out by using local feature extraction and deep neural networks (DNNs). We trained deep generative models using a dictionary-based representation of the image, and then trained deep generative models using a local dictionary representation for each image segment. We further evaluated an image classification method which uses a dictionary-based representation and local feature extraction to train a deep generative model using both locally discriminative and discriminative features. The proposed approach is compared with other methods based on a discriminative model and learned feature extraction.


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