Technical requirements – Labeling Video Data

The era of big data has ushered in an exponential growth of multimedia content, including videos, which are becoming increasingly prevalent in various domains, such as entertainment, surveillance, healthcare, and autonomous systems. Videos contain a wealth of information, but to unlock their full potential, it is crucial to accurately label and annotate the data they contain. Video data labeling plays a pivotal role in enabling machine learning algorithms to understand and analyze videos, leading to a wide range of applications such as video classification, object detection, action recognition, and video summarization.

In this chapter, we will explore the fascinating world of video data classification. Video classification involves the task of assigning labels or categories to videos based on their content, enabling us to organize, search, and analyze video data efficiently. We will explore different use cases where video classification plays a crucial role and learn how to label video data, using Python and a public dataset.

We will learn how to use supervised and unsupervised machine learning models to label video data. We will use the Kinetics Human Action Video dataset to train machine learning models on the labeled data for action detection.

We will delve into the intricacies of building supervised convolutional neural network (CNN) models tailored for video data classification. Additionally, we will explore the application of autoencoders to efficiently compress video data, extracting crucial features. The chapter extends its scope to include the Watershed algorithm, providing insights into its utilization for video data segmentation and labeling. Real-world examples and advancements in video data labeling techniques further enrich this comprehensive exploration of video data analysis and annotation.

In the real world, companies use a combination of software, tools, and technologies for video data labeling. While the specific tools used may vary, some common ones are as follows:

• TensorFlow and Keras: These frameworks are popular for deep learning and provide pre-trained models for video classification and object detection tasks.
• PyTorch: PyTorch offers tools and libraries for video data analysis, including pre-trained models and modules designed for handling video data.
• MATLAB: MATLAB provides a range of functions and toolboxes for video processing, computer vision, and machine learning. It is commonly used in research and development for video data analysis.
• OpenCV: OpenCV is widely used for video data processing, extraction, and analysis. It provides functions and algorithms for image and video manipulation, feature extraction, and object detection.
• Custom-built solutions: Some companies develop their own proprietary software or tools tailored to their specific video data analysis needs.

These are just a few examples of tools used by companies for their use cases in different industries. The choice of tools and technologies depends on the specific requirements, data volume, and desired outcomes of each company.

In this chapter, we’re going to cover the following main topics:

• Capturing real-time video data using Python CV2
• Building supervised CNN models with video data
• Using autoencoders to compress the data to reduce dimensional space and then extracting the important features of the video data
• Using the Watershed algorithm for the segmentation of the video data
• Real-world examples and advances in video data labeling

Technical requirements

In this section, we are going to use the video dataset from the following GitHub link: https://github.com/PacktPublishing/Data-Labeling-in-Machine-Learning-with-Python/datasets/Ch9.

You can find the Kinetics Human Action Video Dataset on its official website: https://paperswithcode.com/dataset/kinetics-400-1.