* Book Topic: Action recognition from videos.
* Recognition Tool: Recurrent Neural Network (RNN) with LSTM (Long-Short Term Memory) layer and fully connected layer.
* Programming Language: Step-by-step implementation with Python in Jupyter Notebook.
* Major Steps: Building a network, training the network, testing the network, comparing the network with an SVM (Support Vector Machines) classifier.
* Processing Units to Execute the Codes: CPU and GPU (on Google Colaboratory).
* Image Feature Extraction Tool: Pretrained VGG16 network.
* Dataset: UCF101 (the first 15 actions, 2010 videos).
* Main Results: For the testing data, the highest prediction accuracy from the RNN is 86.97%, which is a little higher than that from the SVM classifier (86.09%).
* Book Topic: Action recognition from videos.
* Recognition Tool: Recurrent Neural Network (RNN) with LSTM (Long-Short Term Memory) layer and fully connected layer.
* Programming Language: Step-by-step implementation with Python in Jupyter Notebook.
* Major Steps: Building a network, training the network, testing the network, comparing the network with an SVM (Support Vector Machines) classifier.
* Processing Units to Execute the Codes: CPU and GPU (on Google Colaboratory).
* Image Feature Extraction Tool: Pretrained VGG16 network.
* Dataset: UCF101 (the first 15 actions, 2010 videos).
* Main Results: For the testing data, the highest prediction accuracy from the RNN is 86.97%, which is a little higher than that from the SVM classifier (86.09%).
Action Recognition: Step-by-step Recognizing Actions with Python and Recurrent Neural Network
112
Action Recognition: Step-by-step Recognizing Actions with Python and Recurrent Neural Network
112
Product Details
| ISBN-13: | 9781987079081 |
|---|---|
| Publisher: | Barnes & Noble Press |
| Publication date: | 05/26/2019 |
| Series: | Computer Vision and Machine Learning , #2 |
| Pages: | 112 |
| Product dimensions: | 6.00(w) x 9.00(h) x 0.23(d) |