https://ieeexplore.ieee.org/document/8121994/

This Python script extracts features from videos in the YouTube Dataset using a pre-trained ResNet18 model. The extracted features can be used for training and testing LSTM models to perform action recognition.

Getting Started Prerequisites Python 3 PyTorch scikit-learn OpenCV

Download the YouTube Dataset from the following link: https://www.crcv.ucf.edu/data/UCF_YouTube_Action.php

featrues_extraction.py Set the file path to your dataset in data_dir. Define the number of frames to extract features from in num_frames. Define the pre-processing steps for the images in transform. Load the pre-trained ResNet18 model using models.resnet18(pretrained=True).cuda(). Remove the last layer of the ResNet18 model to obtain the feature extractor using torch.nn.Sequential(*list(resnet.children())[:-1]). Loop over the videos in the dataset folder and extract features using the pre-trained ResNet18 model. Split the samples into training and testing sets and convert the labels to numerical labels using a LabelEncoder. Save the features and labels to numpy arrays.

LSTM Action Recognition Model train_LSTM.py This Python script implements a Long Short-Term Memory (LSTM) neural network for action recognition using features extracted from videos in the YouTube Dataset.

Load the features and labels from numpy arrays using torch.from_numpy(np.load('train_features.npy')).float() and torch.from_numpy(np.load('train_labels.npy')). Define the LSTM model using LSTMClassifier or MultiLayerBiLSTMClassifier classes. Define the loss function and optimizer using nn.CrossEntropyLoss() and optim.Adam() respectively. Train the LSTM model using a for loop over the desired number of epochs and batches. LSTM Model LSTMClassifier: a simple LSTM classifier that takes as input a tensor of shape (batch_size, num_frames, input_size) and outputs a tensor of shape (batch_size, num_classes). MultiLayerBiLSTMClassifier: a multi-layer bidirectional LSTM classifier that takes as input a tensor of shape (batch_size, num_frames, input_size) and outputs a tensor of shape (batch_size, num_classes).

@article{ullah2017action, title={Action recognition in video sequences using deep bi-directional LSTM with CNN features}, author={Ullah, Amin and Ahmad, Jamil and Muhammad, Khan and Sajjad, Muhammad and Baik, Sung Wook}, journal={IEEE access}, volume={6}, pages={1155--1166}, year={2017}, publisher={IEEE} }