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Video Classification Cnn Rnn

Developed by keras-io
A hybrid CNN-RNN architecture-based video classification model for action recognition tasks
Video Processing#Action Recognition#Spatiotemporal Modeling#Transfer Learning
Downloads 57
Release Time : 3/2/2022

Model Overview

This model uses CNN to process spatial information of video frames and RNN to handle temporal sequence information. It is trained on the UCF101 dataset and can be used for video action classification tasks.

Model Features

Spatiotemporal Feature Joint Modeling
Extracts spatial features through CNN and processes temporal information with RNN to achieve joint modeling of video spatiotemporal features
Transfer Learning Application
Utilizes pre-trained CNN models for feature extraction to enhance model performance
Lightweight Architecture
Compared to pure 3D convolutional networks, the CNN-RNN architecture has fewer parameters and higher computational efficiency

Model Capabilities

Video Action Recognition
Spatiotemporal Feature Extraction
Multi-class Classification

Use Cases

Smart Security
Abnormal Behavior Detection
Detects abnormal behaviors in surveillance videos such as fighting, falling, etc.
Sports Analysis
Sports Action Recognition
Identifies standard actions in various sports activities
Content Recommendation
Video Content Classification
Automatically tags video content for recommendation systems
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