Human Action Recognition Using Image Preprocessing
This project deals with human action recognition from images through deep learning models. We use datasets of annotated images that show various human interactions, such as sitting, standing, laughing and etc. The main objective is to classify these images into predefined action classes. Several state-of-the-art models, like ResNet50 and InceptionV3, are used to predict highly accurate results.
Project Outcomes
Requirements:
- →Programming Basics in Python and Data Manipulation Techniques.
- →Basic Knowledge of Machine Learning and Deep Learning.
- →Basics about Images Preprocessing-Resizing, Normalizing.
- →Basics of keras and tensorflow to build deep learning models.
- →Experience working with Jupyter Notebooks, if not Google Colab.
- →Data Visualization with Matplotlib and Plotly.
- →The important evaluation metrics of the model are Accuracy and Confusion Matrix.
- →Knows how to work with models like ResNet50 and InceptionV3 that are already trained.
Project Description
This project focuses on deep learning modules to develop a human action recognition system. Thus, actions like sitting, standing, walking or laughing are distinct categories that will serve as inputs for the classification of images. The dataset contains images capturing different human activities tagged with their corresponding categories.
The data preprocessing stage begins with resizing all images to 160 x 160 pixels, normalization of pixel values and contrast enhancement if required. The stages that follow include the conversion of categorical labels to numerical format via LabelEncoder and one hot encoding of the labels to prepare them for the model.
We have the architecture, composed of fine-tuned leads of supremely potent pre-trained deep learning models such as ResNet50 and InceptionV3, to our specific task. During training, early stopping is utilized with the best-selected model based on validation performance. However, during the training process itself, accuracy and loss are monitored.
To evaluate the performance of the trained models, we test them on test data in which we predict labels and compute accuracy. We also produce confusion matrices to visualize class performances. What is achieved in the end is a robust action recognition system that could classify human activity from images accurately.
Learn how to classify human actions from images using deep learning models like ResNet50 and InceptionV3 for security, healthcare and smart home applications.