Video classification in AI: how models learn to see and understand the world in motion
In a world where visual data is becoming increasingly important, video classification is part of our daily lives. We all have the habit of filtering videos on Instagram, on Youtube to select only those that interest us. However, we are also subject to the classifications made by algorithms, without necessarily being aware of them. This is made possible thanks to complex algorithms, which allow machines to “see” and “understand” video sequences. Also, video classification is a technology in its own right: it is no longer limited to the simple recognition of static images, but analysis of movement, context and behavior over time.
In addition, video classifications rely on the careful annotation of visual data and on deep learning models, which aim to break down and interpret complex visual flows. At the crossroads of computer vision and Machine Learning, video classification techniques pave the way for multiple applications, ranging from security and surveillance to medicine, entertainment and transportation industries.
💡 Learn in this article how video classification and data annotation can help you prepare datasets to train and optimize your most complex artificial intelligence models.
What is video classification and why is it important in the AI world?
Video classification is the process by which artificial intelligence systems analyze and categorize video sequences based on specific characteristics, such as actions, objects present, or scene contexts. As described the state of the art on the automatic classification of video sequences published on ResearchGate, this discipline encompasses sophisticated approaches to identify actions, objects, and scenes with increasing precision.
Contrary to the image classification, which looks at isolated images, video classification requires understanding dynamic changes over time. This involves identifying Patterns in movement, to analyze action sequences, and to take into account the temporal continuity between images, which makes the process more complex and requires advanced deep learning models, such as neural networks recurrent and convolutional.
Video classification is essential for AI because it allows machines to understand the world in all its dynamic dimensions. By combining images in their temporal context, AI is able to detect behaviors, interpret gestures, and detect anomalies, which opens up a wide range of applications.
For example, in surveillance, it can identify suspicious activity in real time; in the healthcare sector, it helps analyze medical videos to detect abnormal movements. In addition, with the rise of online videos, video classification has become an indispensable tool for organizing, recommending and making content accessible according to the interests of users.
How is data annotated for video classification?
Annotating data for video classification is a complex process that involves assigning specific labels to video sequences to help AI models recognize and classify actions, objects, or events.
This process involves several key steps:
- Definition of label categories : Before starting, it is essential to determine the relevant annotation categories based on the purpose of the classification. These categories may include actions (such as “running”, “jumping”), objects (such as “vehicle”, “person”), or scenes (such as “park”, “office interior”).
- Split video into segments : To accurately annotate actions and objects, videos are often divided into segments of a few seconds. Each segment represents a specific sequence over time, which makes it possible to analyze the evolution of actions or objects. This breakdown is especially useful when actions or behaviors change during the video.
- Application of time labels : Unlike still images, videos require temporal annotation, i.e. labels applied over specific time ranges. For example, if a person jumps between the 10th and 15th seconds of a video, the annotation should indicate that specific moment to allow the AI model to recognize the action.
- Annotating objects and interactions : In some cases, annotation may include tracking objects through successive frames (or images) to identify specific actions and interactions. This type of annotation often involves drawing ”Bounding Boxes” (bounding boxes) around objects of interest, or ”key points” to analyze detailed movements, such as those of the limbs in sports actions.
- Use of specialized tools and software : Several annotation platforms and tools (like V7, Labelbox, or other Open Source tools) facilitate this process by allowing annotators to add labels, to cut the videos, and to follow the objects in the sequences. These tools also make it possible to manage large quantities of datasets, which is essential for training effective AI models. User interface sections, such as the section Vertex AI from Google Cloud, help organize and manage data annotations by providing important pages such as data sets and model training options.
- Quality and consistency check : Video annotation is error-prone, as it requires detailed interpretation of actions and objects over time. To ensure consistent quality, annotations are regularly reviewed by experts or through automated quality assurance mechanisms.
💡 Through this rigorous process, annotations provide structured data that allows AI models to learn the distinctive characteristics of videos, improving the accuracy and relevance of the classification.
How to analyze and structure video content for optimal classification?
Analysis and structuring of video content for optimal classification rely on several essential steps, which make it possible to transform raw video streams into organized data, ready to be used by AI models. Here are the main steps in this process:
1. Extraction of Frames wrenches
Instead of analyzing each frame of a video, which would be costly in terms of resources, we extract”Frames keys” that represent the most significant moments in the sequence. These Frames are selected based on changes in action or movement, reducing the volume of data to be processed while maintaining the essence of the video. It requires a lot of work Data Curation prerequisite!
2. Segment video into sub-sequences
Segmentation consists of dividing the video into sub-sequences corresponding to different actions or important moments. For example, in a sports video, you could segment game games, breaks, and slow motion. This step helps to isolate specific actions and better structure the data for classification.
3. Annotating actions, objects, and contexts
Once the Frames keys and sub-sequences identified, each element is annotated according to predefined categories, such as actions (walking, jumping), objects (vehicle, person), and context (interior, exterior), and context (interior, exterior). These annotations enrich video content by adding “metadata” to it that serve as guidelines for AI models.
4. Use of pre-treatment techniques
Pre-processing video content includes steps like resizing frames, optimizing colors, or adjusting brightness, which improve visual quality. These adjustments help the AI model focus on important aspects of the image without being distracted by unnecessary variations.
5. Extracting characteristics (Features)
Feature extraction consists of isolating specific information, such as contours, textures, or points of interest in frames, to create feature vectors. These vectors summarize the essential information of each frame and are then used by the algorithms to identify the Patterns and the differences between actions.
6. Time Encoding
To capture the movement and dynamics of a video, time encoding is essential. It allows you to represent the temporal relationships between frames, such as the transition from one movement to another. This is often done through recurrent neural network (RNN) architectures or Transformers, which process information sequentially and enhance the model's ability to understand the flow of actions over time.
7. Grouping into categories of interest
Once the characteristics are extracted and encoded in time, the sub-sequences are grouped into categories of interest defined by the learning model. For example, similar actions, such as walking and running, can be grouped into a larger category of moving actions.
Structuring video content in this way allows AI models to capture the nuances and continuity of footage, improving their ability to accurately classify videos. This approach transforms a series of frames into a structured set of data, facilitating the training of models capable of understanding and interpreting videos in a variety of contexts.
What categories of actions, objects, or scenes should be used for accurate and effective video classification?
For accurate and effective video classification, it is essential to define categories of actions, objects, and scenes that correspond to the specific goals of the application and that are distinct enough for AI models to differentiate them. These categories are often defined during the data/dataset preparation stage: they are simply the labels (or metadata) that you want to assign to a video!
Here are some examples of frequently used categories:
1. Share classes
Actions are the movements or behaviors of individuals or objects in a video. They are an essential category in video classification, especially for surveillance, sports, or behavioral analysis applications. Examples of action categories:
- Travel actions : walking, running, jumping, swimming
- Social interactions : greet, shake hands, speak, applaud
- Sporting activities : throw, hit, dribble, ski
- Specific actions : point, raise your hand, make a sign
- Facial expressions or emotional states : smile, frown, be surprised
💡 These categories allow the model to recognize behaviors and associate them with specific contexts.
2. Object categories
Objects are the material entities present in the video, often needed to identify interactions or contexts. Object categories allow AI models to understand the things that subjects interact with. Examples of object categories:
- Everyday objects : telephone, book, glass, chair
- Vehicles : car, bicycle, airplane, boat
- Animals : dog, cat, bird, horse
- Tools : hammer, screwdriver, brush
- Sports products : ball, racket, gloves, helmet
💡 These object categories help models identify interactions or activities based on the object (for example, “playing tennis” by detecting a racket and a ball).
3. Scene categories
Scenes provide the environmental context for the action or interactions observed. Detecting the scene in which the action is taking place helps the AI adjust its interpretation of video content. Examples of scene categories:
- Indoor environments : home, office, store, gym
- Outdoor environments : park, street, beach, forest
- Transport and mobility : station, airport, highway, subway
- Public events : concert, event, sports competition
- Natural scenes : mountain, lake, desert, garden
💡 These scene categories are essential for differentiating contexts and refining the understanding of the model (for example, “running in a park” versus “running on a treadmill”).
4. Combined (or contextual) categories
Some applications require categories that combine multiple dimensions, such as specific actions in given environments or interactions between objects and people. Examples of combined categories:
- Driving in traffic : includes driving actions and surrounding objects such as cars
- Classroom education : actions like writing, listening, raising hands, and interior scenes in a classroom
- Industrial safety : includes specific actions (such as welding, using a machine) in industrial environments and with particular objects (such as safety equipment)
💡 These categories allow for more nuanced analysis and are useful for specialized applications, such as security, education, or medicine.
By choosing specific categories of actions, objects, and scenes, the classification model is provided with clear guidelines for organizing and interpreting video content. This categorization structure improves the accuracy of the classification and makes the models more suitable for specific use cases.
How do you choose the right keywords to use to structure and optimize video classification annotations?
Choosing the right keywords to structure and optimize video classification annotations is critical to ensuring that AI models can interpret and classify videos accurately and contextually relevant. Here are the main criteria and steps for selecting effective keywords:
1. Understanding classification goals
Before selecting keywords, it is important to clearly define the goals of the classification. For example, a surveillance app will require keywords related to suspicious actions, while a sports app will focus on specific movements.
Keywords should reflect the behaviors, objects, or scenes that are essential to detect to meet the needs of the final application.
2. Choose specific and descriptive keywords
Keywords should be specific enough to avoid ambiguities. For example, instead of “movement,” a keyword like “run” or “jump” will be more informative.
Avoid generic words that could lead to misclassification. Using specific terms for each action or object category improves the consistency of annotations and better guides the model.
3. Consider categories of actions, objects, and scenes
Use keywords adapted to the various categories needed, such as actions (e.g. “walking”, “talking”), objects (e.g. “vehicle”, “telephone”), and scenes (e.g. “outdoor”, “gym”).
This makes it possible to organize the annotations according to the needs of video classification and to optimize the results by providing clear guidelines for learning the model.
4. Use temporal keywords for sequential actions
Video actions often involve time sequences (start, unfolding, end of an action). Using keywords that capture this time dimension, such as “start,” “transition,” or “end,” is useful for the model to understand the continuity of actions in a sequence.
For example, keywords like “start running”, “stop running” can help structure the annotation in a more nuanced way.
5. Use keywords adapted to the cultural and application context
Some keywords can have varied meanings depending on the cultural or application context. It is important to choose terms that correspond to the interpretation expected in the specific context of the application.
For example, in a medical context, keywords such as “pulse check” or “auscultate” are precise and appropriate, while more generic words would be insufficient.
6. Search for standardized or recognized keywords in the field
Use standardized terms when possible, for example those commonly used in computer vision libraries, to facilitate consistent annotations and comparison of results.
Conventions established in specialized fields (such as sports, medicine, or security) also make it easier for models to generalize knowledge.
7. TTest and refine keywords based on classification results
Once the annotations are applied, it is useful to test the performance of the model and to refine the keywords based on the results. Adjustments can be made to remove ambiguities or to introduce new, more representative keywords.
This means regularly reviewing the annotations and adapting the keywords according to the classification errors detected.
By choosing specific keywords, adapted to the context and tested, we improve the structure of the annotations, which makes it possible to optimize the performance of AI models for video classification. These keywords play a central role in learning models, as they serve as clear guidelines for understanding and organizing video footage effectively.
Train a classification model
Training a classification model is a required step in improving the accuracy of video classification. This process is based on two main methods: machine learning (AutoML) and supervised learning. AutoML makes it possible to create classification models without requiring in-depth machine learning knowledge, by automating the steps of selecting algorithms and optimizing hyperparameters. In contrast, supervised learning requires provide labeled examples to train the model, which means more human intervention.
To ensure effective training, it is essential to have a quality data set. This data should include videos that are tagged with relevant categories, such as specific actions, objects, or scenes. Data quality is essential because it directly influences the performance of the model. A well-annotated data set allows the model to learn the distinctive characteristics of videos and improve classification accuracy.
The choice of training parameters is also important. This involves selecting the appropriate training method, distributing the data in a balanced manner between training and validation, and adjusting hyperparameters to optimize model performance. For example, lot size, learning rate, and number of epochs are hyperparameters that can be adjusted to improve training.
In summary, training a video classification model requires a combination of quality data, adapted training methods, and precise hyperparameter settings. This process makes it possible to develop models that can classify videos with great precision, paving the way for varied and innovative applications.
Applications of classification
Video classification offers a multitude of applications in a variety of fields, transforming the way we interact with video content. Here are some of the most common and impactful applications:
- Content recommendation : Through video classification, platforms can recommend relevant content to users based on their preferences and viewing history. For example, a user who frequently watches cooking videos will be offered similar recipes or cooking shows.
- Video search : Classification greatly improves video search by allowing results to be filtered according to specific criteria, such as category, genre, or quality. This makes it easier to discover relevant content and reduces the time spent looking for videos.
- Content moderation : Video classification plays a crucial role in content moderation by detecting and removing inappropriate or offensive videos. Algorithms can identify violent, hateful, or explicit content, ensuring a safer online environment for users.
- Targeted advertising : By understanding user interests through video classification, advertisers can target their ads more effectively. For example, someone watching fitness videos could receive ads for sports equipment or gym memberships.
- Creating collections : Classification allows you to create video collections based on specific criteria, such as category or genre. This is especially useful for streaming platforms that want to organize their content thematically, making it easier for viewers to navigate.
Conclusion
In summary, video classification is a powerful tool for improving the quality and relevance of video content. It not only optimizes the user experience, but also meets specific needs in a variety of areas, from content recommendation to moderation and targeted advertising. Thanks to these applications, video classification continues to transform how we interact with digital content.
As an artificial intelligence technology, video classification opens up major opportunities for analyzing, organizing, and interpreting complex video sequences. By using advanced annotation methods and structuring data strategically, it is possible to transform video streams into actionable information for various types of projects.
Careful choice of keywords, categories, and labels allows AI models to accurately detect actions, objects, and scenes, and to interpret the temporal relationships inherent in videos. This ability to “see” and understand the world in motion gives AI models practical applications in a variety of fields, from surveillance to medicine.
