What is Video Classification in AI?

Video Classification in AI is the process of automatically identifying and categorizing actions, objects, scenes, or events within video content using machine learning models. By analyzing a series of frames over time, video classification enables AI systems to recognize patterns and provide meaningful labels for various types of video content.

Video Classification
Figure 1 - Video Classification

Where can you find AI Video Classification models

This is the link to use to filter Hunggingface models for Video Classification:

https://huggingface.co/models?pipeline_tag=video-classification&sort=trending

Our favourite Model Authors:

The most interesting Video Classification project

One of the most interesting Video Classification projects is called UniFormer .

UniFormer models are trained on Kinetics and Something-Something at resolution 224x224. It was introduced in the paper UniFormer: Unified Transformer for Efficient Spatial-Temporal Representation Learning by Li et al, and first released in this repository.

Model description

The UniFormer is a type of Vision Transformer, which can seamlessly integrate merits of convolution and self-attention in a concise transformer format. It adopt local MHRA in shallow layers to largely reduce computation burden and global MHRA in deep layers to learn global token relation.

Without any extra training data, UniFormer achieves 86.3 top-1 accuracy on ImageNet-1K classification. With only ImageNet-1K pre-training, it can simply achieve state-of-the-art performance in a broad range of downstream tasks. UniFormer obtains 82.9/84.8 top-1 accuracy on Kinetics-400/600, and 60.9/71.2 top-1 accuracy on Something-Something V1/V2 video classification tasks. It also achieves 53.8 box AP and 46.4 mask AP on COCO object detection task, 50.8 mIoU on ADE20K semantic segmentation task, and 77.4 AP on COCO pose estimation task.

https://huggingface.co/Sense-X/uniformer_video

Understanding How Video Classification Works

Video classification involves processing and analyzing multiple frames over time to identify key features that characterize the video’s content. This is typically achieved by combining spatial analysis (understanding the objects or scenes within individual frames) with temporal analysis (tracking how these elements change over time). The main stages involved in video classification are:

  • Data Collection: A large dataset of labeled videos is used to train video classification models, covering a diverse set of categories, actions, or events to ensure robust learning.
  • Feature Extraction: Video frames are analyzed to extract both spatial (frame-specific) and temporal (sequence-related) features. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are commonly used to capture these attributes.
  • Model Training: Using the extracted features, the model is trained to learn relationships between frames and accurately classify the entire video. This training often involves deep learning architectures like 3D CNNs or transformers that are designed to process video sequences.
  • Classification: Once trained, the model can classify new videos by analyzing the patterns it has learned, providing labels or categories based on recognized patterns.

Examples of Models Used in Video Classification

Some of the prominent models and techniques used in video classification include:

  • 3D Convolutional Neural Networks (3D CNNs): These extend 2D CNNs to capture spatial and temporal information simultaneously by applying 3D convolution filters across frames.
  • Two-Stream Networks: This approach utilizes two neural networks, one for spatial information (frames) and one for temporal information (optical flow), to capture both static and dynamic aspects of a video.
  • Recurrent Neural Networks (RNNs) and LSTMs: RNNs, particularly Long Short-Term Memory (LSTM) networks, are used to model temporal dependencies across frames, making them effective for analyzing sequences over time.
  • Transformers: With their attention-based mechanism, transformers can capture relationships within frames and across sequences, providing state-of-the-art results in video classification tasks.
  • I3D (Inflated 3D ConvNet): This model adapts 2D convolutional filters for 3D use, extending successful image recognition models for video classification.

Applications of Video Classification in AI

Video classification has a wide range of applications across industries, including:

1. Content Moderation on Social Media

Social media platforms use video classification to detect inappropriate or harmful content. This technology automatically scans and flags videos containing explicit, violent, or other unsuitable content, ensuring a safe experience for users.

2. Video Surveillance and Security

In security applications, video classification helps monitor video feeds to detect unusual activities, such as intrusions or unattended objects. By classifying events or behaviors, these systems alert authorities about potential security incidents in real time.

3. Autonomous Driving

Video classification assists autonomous vehicles in recognizing objects, road signs, and pedestrians. By classifying the environment in real time, AI in self-driving cars can make informed decisions to ensure safety on the road.

4. Healthcare and Medical Diagnosis

In healthcare, video classification is used to analyze medical video content, such as ultrasound or endoscopic videos. AI can help classify patterns indicative of medical conditions, assisting doctors in early diagnosis and treatment.

5. Sports Analytics

Video classification enables real-time analysis of sports footage, categorizing events like goals, fouls, or player movements. This allows coaches and analysts to gain insights into player performance and game strategies.

6. E-Learning and Education

Educational platforms use video classification to segment instructional videos, making it easier for students to locate specific topics. Video classification also helps in providing interactive and organized content for better learning experiences.

7. Retail and Customer Insights

Video classification helps retailers analyze customer behavior, such as store navigation patterns or product interactions, enabling data-driven decisions to enhance customer experience and sales strategies.

8. Wildlife Monitoring and Conservation

Video classification is used in ecological research to identify animal species and behaviors captured on video. This enables researchers to monitor wildlife habitats, track population changes, and study animal behavior patterns.

9. Film and Media Industry

In the media industry, video classification organizes large volumes of video content by genres, scenes, or topics. This makes it easier for production teams, editors, and consumers to find specific types of content.

10. Human Activity Recognition

Video classification plays a key role in applications involving human activity recognition, such as monitoring fitness activities, gesture recognition for human-computer interaction, or monitoring elderly individuals for fall detection.

Challenges in Video Classification

Despite its advancements, video classification in AI has several challenges:

  • Large Computational Requirements: Video data is computationally intensive, requiring extensive resources to process and analyze multiple frames over time.
  • Data Availability and Labeling: Collecting and labeling large datasets for video classification is challenging and costly, especially for specific or rare events.
  • Temporal Dependencies: Accurately capturing the temporal relationships across frames is essential but challenging, as subtle variations in sequences can change the interpretation.
  • Real-Time Processing: Many applications require real-time classification, demanding fast and efficient algorithms to avoid latency, especially in fields like autonomous driving.
  • Ethical Considerations: The use of video classification in surveillance and privacy-sensitive applications raises ethical concerns regarding user consent and data privacy.

Future Developments in Video Classification

The future of video classification in AI looks promising, with ongoing research in several areas:

  • Advancements in Model Architectures: Emerging architectures, including improved transformers and hybrid CNN-RNN models, promise higher accuracy and efficiency in video classification.
  • Real-Time Processing Improvements: As hardware advances, achieving real-time video classification with lower latency will make it feasible for applications like autonomous driving and video surveillance.
  • Transfer Learning and Pre-trained Models: Pre-trained models for video classification will make it easier for researchers to apply high-quality models to various domains without the need for massive labeled datasets.
  • Ethical and Privacy-Respectful Models: Future models will likely incorporate mechanisms to address ethical concerns, balancing the need for surveillance with user privacy rights.
  • Multi-modal Data Fusion: Integrating video with other data types (e.g., audio, sensor data) will enable more contextually aware and robust classification systems.

Conclusion

Video classification in AI represents a powerful tool for extracting insights and automating processes across diverse fields. From content moderation and security to medical diagnosis and entertainment, video classification is transforming industries by enabling real-time and large-scale video content analysis. With advancements in model architectures, real-time capabilities, and ethical frameworks, video classification will continue to expand in capability and application, offering more efficient and insightful ways to understand video data.

Additional Resources for Further Reading

How to setup a Video classification LLM on Ubuntu Linux

If you are ready to setup your first Video classification system follow the instructions in our next page:

How to setup a Video classification system

Image sources

Figure 1: https://www.v7labs.com/blog/video-classification-guide

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