[PDF] Top 20 Two-Stream Convolutional Networks for Action Recognition in Videos
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Two-Stream Convolutional Networks for Action Recognition in Videos
... video recognition was proposed with pre-defined spatio-temporal filters in the first ...a convolutional RBM and ISA were used for unsupervised learning of spatio-temporal features, which were then plugged ... See full document
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Exploiting textures for better action recognition in low-quality videos
... introducing two essential types of local features: shape, in the form of histogram of oriented gra- dients (HOG), and motion, in the form of histogram of optical flow ...with videos captured in a relatively ... See full document
18
Learning action recognition model from depth and skeleton videos
... Depth Videos: Although skeleton based meth- ods achieve impressive action recognition accuracies on human action datasets, it is not sufficient to only use the skeletal data to model actions, ... See full document
10
Simple and Complex Human Action Recognition in Constrained and Unconstrained Videos
... the action recognition ...an action from a fixed ...the videos can be captured using different points of ...same action and different viewpoints are utilized to train a single ... See full document
165
Human action recognition using transfer learning with deep representations
... and videos over the internet, demands for high accuracies, and computational efficiencies are ...object recognition, image classification and human action recognition ... See full document
7
Pedestrian detection with motion features via two-stream ConvNets
... of two-stream convolutional networks, each of which are trained separately for spatial and temporal ...temporal stream takes as input the difference in frames that are weakly stabilized ... See full document
13
Temporal scale Convolutional Networks for Human Action Recognition Based on Key Frame Extraction
... Human action recognition is an important part of intelligent video ...the two-stream convolutional ...long-term action recognition, existing approaches mainly use video ... See full document
6
I Know the Relationships: Zero-Shot Action Recognition via Two-Stream Graph Convolutional Networks and Knowledge Graphs
... many action categories in HMDB51 are not sensitive to objects, such as the actions run, walk, sit, and ...all videos, which help us remove rare and unreliable objects in building knowledge ... See full document
9
Dense ResNet based Human Action Recognition using Novel Trajectory Maps on 3D Skeletal Data
... sequence action recognition is therefore not enough to solve the challenges in HAR, particularly in truthful ...the recognition of RGB, depth, or combination of these data categories (RGB-D) have ... See full document
10
Deep Manifold Structure Transfer for Action Recognition
... effective action recognition approaches root in powerful learning methods, particulary the deep CNN approaches [25], ...learn action representations from videos are categorized as four ... See full document
13
Asymmetric 3D Convolutional Neural Networks for Action Recognition
... the videos to produce large performance improvements over previously ...The Two-stream LSTM [46] stacks multiple LSTM lay- ers to capture dynamic information in a hierarchical ...The two ... See full document
36
Vision based human action recognition using machine learning techniques
... Inspired by the dense sampling in image classification, the concept of dense trajectories for action recognition from videos was introduced [68]. The authors sampled the dense points from each image ... See full document
173
Opisthenar : hand poses and finger tapping recognition by observing back of hand using embedded wrist camera
... 2D Convolutional Neural Network (CNN) works well for static hand poses but not for dynamic finger ...current Convolutional Networks (LRCN) and later we settled at ii) Two-Stream ... See full document
9
Inter-Class Angular Loss for Convolutional Neural Networks
... Baseline Networks To show the adaptability of ICAL to different networks with different classification tasks, we apply ICAL to two types of convolutional neural networks (CNNs) and ... See full document
8
Face recognition with Bayesian convolutional networks for robust surveillance systems
... face recognition approaches Although machine learning techniques for facial recogni- tion have provided decent results, these techniques do not perform well under unconstrained ...face recognition. Sun et ... See full document
10
Ensemble feature learning for material recognition with convolutional neural networks
... material recognition is that large-scale databases combined with convolutional neural networks ...and recognition and has recently achieved the state-of-the-art results in object ... See full document
11
Handwritten Digit Recognition using Convolutional Neural Networks
... We can implement Convolutional neural networks in PYTHON/MATLAB. In our implementation we will be using python because we have our keras deep learning library built in python . So by using keras models we ... See full document
6
Convolutional Neural Networks for Clothing Image Style Recognition
... of two fully connected layers accounts for more than 85% of all parameters, which seriously affects the overall speed of the ...a convolutional neural network, the convolutional layer mainly extracts ... See full document
6
Generalizing convolutional neural networks for pattern recognition tasks
... the two-dimensional (2-D) image topology of input changes. This makes CNN robust against changes of input patterns, including translation, scaling, and rotation. This robustness is due to the built-in invariance ... See full document
11
Pattern Recognition with Convolutional Neural Networks: Humpback Whale Tails
... The term Artificial Intelligence has been raised historically from different points of view: the ability to think or the ability to act intelligently [1]. On the one hand, the first focuses on the approach of a human ... See full document
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