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Methods for video classification
video-classification's Introduction
- HOG(Histogram of Grident)
- HOF(Histogram of Feature)
- BoF(Bag of Feature)
- Dense Point Trajectory
- Motion Boundary History(MBH)
- Trajectory Fisher Vector
- ConvNet Feature
- Stacked OpticalFlow
- Action Recognition by Dense Trajectories. [Paper] [Code]
- Dense trajectories and motion boundary descriptors for action recognition. [Paper] [Python Code]
- Large-scale Video Classification with Convolutional Neural Network. [Project]
- Learning SpatialTemporal Feautres with 3D Convolutional Networks. [Paper]
- 3D Convolutional Neural Networks for Human Action Recognition. [Paper]
- Two-Stream Convolutional Networks for Action Recognition in Videos. [Paper][Code]
- Two-Stream SR-CNNs for Action Recognition in Videos. [Paper][Code]
- Trajectory-Pooled Deep Convolutional Descriptor. [Paper]
- Temporal Segment Networks: Towards Good Practices for Deep Action Recognition. [Paper]
- Learnable pooling with Context Gating for video classification.
- Finding Action Tubes. [Paper] [Code]
- Multi-region Two-Stream R-CNN for Action Detection.
- Temporal Action Detection with Structured Segment Networks.
- UntrimmedNets for Weakly Supervised Action Recognition and Detection
- R-CNNs for Pose Estimation and Action Detection.
- Multi-Stream Multi-Class Fusion of Deep Networks for Video Classification. [Paper]
- Real-time Action Recognition with Enhanced Motion Vector CNNs.[Project]
- Convolutional Two-Stream Network Fusion for Video Action Recognition. [Paper][Code]
- ActionVLAD: Learning spatio-temporal aggregation for action classification. [Project]
video-classification's People
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