In a world where visual data is becoming increasingly important, 🔗 video classification is part of our daily lives. We're all used to filtering videos on Instagram, on Youtube to select only those that interest us. For all that, we are also subject to classifications made by algorithms, without necessarily being aware of it. This is made possible by complex algorithms that enable machines to "see" and "understand" video sequences. Video classification is therefore a technology in its own right: it no longer simply recognizes static images, but analyzes movement, context and behavior over time.

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In addition, video classifications rely on the careful annotation of visual data and on deep learning models, which aim to decompose and interpret complex visual streams. At the crossroads of computer vision and 🔗 Machine Learningvideo classification techniques open up a wide range of applications, from security and surveillance to medicine, entertainment and transportation.

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💡 Find out in this article how video classification and data annotation can help you prepare datasets to train and optimize your most complex artificial intelligence models.

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What is video classification and why is it so important in the AI world?

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Video classification is the process by which artificial intelligence systems analyze and categorize video sequences according to specific features, such as actions, objects present, or scene contexts. As described 🔗 the state of the art on automatic classification of video sequences published on ResearchGatethis discipline encompasses sophisticated approaches to identifying actions, objects and scenes with increasing precision.

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Unlike 🔗 image classificationwhich examines isolated images, video classification requires an understanding of dynamic changes over time. This involves identifying patterns in movement, analyzing sequences of actions, and taking into account temporal continuity between images, making the process more complex and requiring advanced Deep Learning models, such as 🔗 neural networks and convolutional neural networks.

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Video classification is essential for AI, enabling machines to understand the world in all its dynamic dimensions. By associating images in their temporal context, AI is able to detect behaviors, interpret gestures and detect anomalies, opening up a wide range of applications.

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For example, in surveillance, it enables suspicious activity to be spotted in real time; in the healthcare sector, it helps to analyze medical videos to detect abnormal movements. What's more, with the rise of online video, video classification has become an indispensable tool for organizing, recommending and making content accessible according to users' interests.

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How is data annotated for video classification?

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Data annotation 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.

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There are several key stages in this process:

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1. Defining label categories: Before getting started, it's essential to determine the relevant annotation categories according to the purpose of the classification. These categories can include actions (such as "run", "jump"), objects (such as "vehicle", "person"), or scenes (such as "park", "office interior").
2. Cutting video into segments: To precisely annotate actions and objects, videos are often divided into segments of a few seconds. Each segment represents a specific sequence in time, making it possible to analyze the evolution of actions or objects. This is particularly useful when actions or behaviors change over the course of the video.
3. Applying temporal labels: Unlike still images, videos require temporal annotation, i.e. labels applied to specific time frames. For example, if a person jumps between the 10th and 15th second of a video, the annotation must indicate this precise moment to enable the AI model to recognize the action.
4. Object and interaction annotation: In some cases, annotation can include tracking objects through successive frames (or images) to identify specific actions and interactions. This type of annotation often involves drawing 🔗 "bounding boxes"around objects of interest, or 🔗 "key points" to analyze detailed movements, such as those of limbs in sporting actions.
5. Use of specialized tools and software: Several annotation platforms and tools (such as V7, Labelbox or other Open Source tools) facilitate this process by allowing 🔗 annotators to add labels, trim videos, and track objects in 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 🔗 Vertex AI section of Google Cloud, help organize and manage data annotations by providing important pages such as those for datasets and model training options.
6. Quality control and consistency: Video annotation is error-sensitive, requiring detailed interpretation of actions and objects over time. To ensure consistent quality, annotations are regularly reviewed by experts or via automated quality assurance mechanisms.

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💡 Thanks to this rigorous process, annotations provide structured data that enable AI models to learn the distinctive features of videos, improving the accuracy and relevance of classification.

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How to analyze and structure video content for optimal classification?

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🔗 The analysis and structuring of video content for optimal classification relies on several essential steps, which transform raw video streams into organized data, ready for use by AI models. Here are the main steps in this process:

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1. Extraction of key frames‍

Rather than analyzing every frame of a video, which would be resource-intensive, we extract" keyframes " 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 retaining the essence of the video. This requires a great deal of 🔗 Data Curation beforehand!

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2. Segment video into subsequences‍

Segmentation consists in dividing the video into subsequences corresponding to different actions or important moments. For example, in a sports video, we might segment game parts, pauses and slow-motion sequences. This step helps to isolate specific actions and better structure the data for classification.

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3. Annotation of actions, objects and contexts‍

Once the key frames and subsequences have been identified, each element is annotated according to predefined categories, such as actions (walking, jumping), objects (vehicle, person), and context (indoor, outdoor). These annotations enrich the video content, adding "metadata" that serve as reference points for the AI models.

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4. Use of pre-treatment techniques‍

Pre-processing video content includes steps such as resizing frames, optimizing colors or adjusting brightness, all of which improve visual quality. These adjustments help the AI model to focus on the important aspects of the image without being distracted by unnecessary variations.

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5. Extraction of characteristics (features)‍

Feature extraction involves 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 patterns and differences between actions.

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6. Time encoding‍

To capture the movement and dynamics of a video, time encoding is essential. It enables temporal relationships between frames to be represented, such as the transition from one movement to another. This is often achieved using recurrent neural network (RNN) or Transformer architectures, which process information in sequence and enhance the model's ability to understand the flow of actions over time.

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7. Grouping into interest categories

‍Oncethe features have been extracted and temporally encoded, the subsequences 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 broader category of movement actions.

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Structuring video content in this way enables AI models to grasp the nuances and continuity of sequences, improving their ability to classify videos accurately. 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 diverse contexts.

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What categories of actions, objects or scenes should you use for accurate and efficient video classification?

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For accurate and efficient video classification, it's essential to define categories of actions, objects and scenes that correspond to the specific objectives of the application, and that are sufficiently distinct for AI models to be able to differentiate between them. These categories are often defined during the data/dataset preparation stage: they are simply the labels (or metadata) you wish to assign to a video!

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Here are some examples of frequently used categories:

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1. Share categories

Actions are the movements or behaviors of individuals or objects in a video. They are an essential category in video classification, particularly for surveillance, sports or behavioral analysis applications. Examples of action categories :

• Movement: walking, running, jumping, swimming
• Social interaction: greeting, shaking hands, speaking, clapping
• Sports activities: throwing, hitting, dribbling, skiing
• Specific gestures: pointing, raising the hand, making a sign
• Facial expressions or emotional states: smiling, frowning, surprised

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💡 These categories enable the model to recognize behaviors and associate them with specific contexts.

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2. Object categories

Objects are the material entities present in the video, often needed to identify interactions or contexts. Object categories enable AI models to understand the elements with which subjects interact. Examples of object categories :

• Everyday objects: telephone, book, glass, chair
• Vehicles: car, bicycle, plane, boat
• Animals: dog, cat, bird, horse
• Tools: hammer, screwdriver, paintbrush
• Sports products: ball, racket, gloves, helmet

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💡 These object categories help models identify object-based interactions or activities (for example, "playing tennis" by detecting a racket and a ball).

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3. Scene categories

Scenes provide the environmental context for the observed action or interaction. Detecting the scene in which the action takes place helps AI to adjust its interpretation of the video content. Examples of scene categories:

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• Indoor environments: home, office, store, gym
• Outdoor environments: park, street, beach, forest
• Transport and mobility: station, airport, freeway, metro
• Public events: concerts, demonstrations, sports competitions
• Natural scenes: mountain, lake, desert, garden

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💡 These scene categories are essential for differentiating contexts and refining understanding of the model (e.g. "running in a park" vs. "running on a treadmill").

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4. Combined (or contextual) categories

Some applications require categories that combine several 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 such as writing, listening, raising hands, and interior scenes in a classroom.
• Industrial safety: includes specific actions (such as welding, operating a machine) in industrial environments and with specific objects (such as safety equipment).

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💡 These categories enable a more nuanced analysis and are useful for specialized applications, such as security, education, or medicine.

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Choosing precise categories of actions, objects and scenes provides the classification model with clear guidelines for organizing and interpreting video content. This categorization structure improves the accuracy of classification and makes models more suitable for specific use cases.

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How do you choose the right keywords to use to structure and optimize annotations in video classification?

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Choosing the right keywords to structure and optimize annotations in video classification is essential to ensure that AI models can interpret and classify videos accurately and contextually relevant. Here are the main criteria and steps for selecting effective keywords:

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1. Understanding classification objectives

Before selecting keywords, it's important to clearly define the objectives of the classification. For example, a surveillance application will require keywords related to suspicious actions, while a sports application will focus on specific movements.

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The keywords must reflect the essential behaviors, objects or scenes to be detected in order to meet the needs of the final application.

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2. Choose specific, descriptive keywords

Keywords should be precise enough to avoid ambiguity. For example, instead of "movement", a keyword like "run" or "jump" will be more informative.

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Avoid generic words that could lead to classification errors. Using precise terms for each action or object category improves annotation consistency and better guides the model.

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3. Consider categories of actions, objects and scenes

Use keywords adapted to the different categories required, such as actions (e.g. "walking", "talking"), objects (e.g. "vehicle", "telephone"), and scenes (e.g. "outdoors", "gym").

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This allows annotations to be organized according to the needs of video classification, and optimizes results by providing clear cues for model learning.

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4. Use temporal keywords for actions in sequence

Actions in video often involve temporal sequences (start, progress, end of an action). Using keywords that capture this temporal dimension, such as "start", "transition", or "end", is useful for the model to understand the continuity of actions in a sequence.

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For example, keywords such as "start running", "stop running" can help to structure the annotation in a more nuanced way.

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5. Use keywords adapted to the cultural and application context

Some keywords can have different 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.

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For example, in a medical context, keywords such as "take pulse" or "auscultate" are precise and appropriate, while more generic words would be insufficient.

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6. Search for standardized or recognized keywords in the field

Use standardized terms whenever possible, such as those commonly used in computer vision libraries, to facilitate consistent annotation and comparison of results.

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Conventions established in specialized fields (such as sports, medicine or safety) also enable models to generalize knowledge more easily.

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7. Testand refine keywords based on classification results

Once the annotations have been applied, it is useful to test the model's performance and refine the keywords according to the results. Adjustments can be made to remove ambiguities or to introduce new, more representative keywords.

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This means regularly reviewing annotations and adapting keywords according to the classification errors detected.

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By choosing specific, context-sensitive and tested keywords, the structure of the annotations is improved, optimizing the performance of AI models for video classification. These keywords play a central role in model learning, as they serve as clear cues for understanding and organizing video sequences efficiently.

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Training a classification model

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Training a classification model is a necessary 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 knowledge of Machine Learning, by automating the algorithm selection and hyperparameter optimization stages. Supervised learning, on the other hand, requires 🔗 provide labeled examples to train the modelwhich implies greater human intervention.

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To ensure effective training, it's essential to have a quality data set. This data must include videos labeled with relevant categories, such as specific actions, objects or scenes. Data quality is paramount, as it directly influences model performance. A well-annotated dataset enables the model to learn the distinctive features of videos and improve classification accuracy.

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The choice of training parameters is also important. This involves selecting the appropriate training method, dividing the data evenly between training and validation, and adjusting the hyperparameters to optimize model performance. For example, batch size, learning rate and number of epochs are hyperparameters that can be adjusted to improve training.

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In short, training a video classification model requires a combination of high-quality data, suitable training methods and precise hyperparameter settings. This process makes it possible to develop models capable of classifying videos with great accuracy, paving the way for varied and innovative applications.

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Classification applications

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Video classification offers a multitude of applications in various fields, transforming the way we interact with video content. Here are a few of the most common and impactful applications:

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• Content recommendation: Thanks to 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 enhances video search by allowing results to be filtered according to specific criteria, such as category, genre or quality. This facilitates the discovery of relevant content and reduces the time spent searching for videos.
• Content moderation: Video classification plays a crucial role in content moderation, 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 users' interests through video classification, advertisers can target their ads more effectively. For example, someone watching fitness videos might 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 particularly useful for streaming platforms wishing to organize their content thematically, making it easier for viewers to navigate.

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Conclusion

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In short, video classification is a powerful tool for improving the quality and relevance of video content. Not only does it optimize the user experience, it can also meet specific needs in a variety of areas, from content recommendation to moderation and targeted advertising. Thanks to these applications, video classification continues to transform the way we interact with digital content.

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As an artificial intelligence technology, video classification opens up major perspectives for analyzing, organizing and interpreting complex video sequences. By using advanced annotation methods and strategically structuring data, it is possible to transform video streams into information that can be exploited for various types of project.

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The rigorous choice of keywords, categories and labels enables 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.