Artificial intelligence (AI) has made tremendous advances in recent years, largely thanks to improvements in deep learning techniques and the availability of large datasets to train machine learning models. Especially in computer vision, massive amounts of labeled image and video data have proven invaluable for developing advanced AI systems.

As AI researchers and developers look to train increasingly sophisticated computer vision algorithms, identifying suitable visual data sources is crucial. Not all datasets are created equal – the quality, size, diversity, and labeling accuracy of the video clips can significantly impact the performance of the trained model.

So, which video source is the best fit to train the ai software? Selecting the ideal video source to train a computer vision AI involves evaluating several key factors:

Diversity of Visual Concepts

A critical consideration is the diversity of visual concepts in the video dataset. The more varied the objects, actions, scenes, viewpoints, lighting conditions, etc, the more robust the trained model will generalize to real-world scenarios.

For instance, a driving dataset with video captured in bright daylight, overcast conditions, nighttime, sun glare, rural roads, busy highways, and different weather can expose the AI to many driving visuals. In contrast, a dataset with mostly similar highway driving under optimal conditions would fail to account for many everyday driving situations.

Ideally, the video source will cover the full scope of visual concepts the AI application must handle. Training computer vision for autonomous vehicles requires radically different data than training a social robot to navigate indoor spaces and interact with people.

Match the dataset diversity to the problem at hand for optimal model performance.

Quantity of Training Examples

In deep learning, models with higher capacity can capture more complex patterns given sufficient data. As such, the overall size of the training video dataset makes a big difference – the more varied examples, the better.

For example, datasets like YouTube-8M contain millions of YouTube video IDs, while Something-Something contains just 220,000 video clips. All else being equal, the larger dataset provides more Visual learning examples to train a richer deep neural network model.

However, diversity and labeling quality are also crucial – so more data is not always better. But a good rule of thumb is to prefer larger video datasets to expose the model to more situations and edge cases during training.

Labeling Accuracy

Deep learning models are only as good as their training data. Noisy, imprecise, or incorrect labels severely diminish the accuracy of the trained model.

Some factors to evaluate around video labeling accuracy:

• Human vs automated labeling – Manually labeled data is generally of higher quality than auto-labeled data, which can propagate errors. Human raters should also be adequately trained to ensure consistency.
• Label verification – There should be checks to verify label quality through inter-rater agreement, testing label consistency, and examining examples flagged as unclear.
• Label distribution – Labels should cover the entire distribution of classes the model must recognize without significant class imbalance skewing the training.
• Clip sampling – When training the model, the selected clips must adequately represent the desired labels to avoid ambiguity and confusion.
• Clip duration – Short clips with incomplete actions and unclear context can introduce labeling errors. Optimal clip duration depends on the complexity of the visual concepts.

Prioritize datasets with demonstrable efforts to maximize labeling accuracy for trustworthy model training.

Relevance to the Problem

The video data needs to match the computer vision problem at hand – don’t use a generic image dataset to train a specialized industrial inspection algorithm.

For example, a social robot’s computer vision system requires videos depicting human activities, gestures, indoor scenes, etc. A fruit sorting AI needs training videos showing various fruits, angles, sizes, colors, freshness, etc., in relevant agricultural environments.

Consider how closely the dataset matches the practical use case, environment, and visual concepts required. The more realistic and specific to the problem, the better the model transfer will be. Generic datasets have their place for pre-training, but custom video sources are ideal.

Licensing and Accessibility

Public datasets under permissive licensing (e.g., Creative Commons) offer the most flexibility and ease of access for researchers, students, and companies to prototype and experiment rapidly.

Restrictively licensed or private datasets add hurdles to accessing and using the data and slow research progress. They also limit the sharing of trained models, which goes against the open spirit of advancing AI innovation.

Ideally, look for dataset creators that make their data freely available and shareable—the less red tape to get started, the better.

Budget Constraints

The costs of acquiring dataset rights and licensing fees for large commercial applications can mount quickly. Some popular datasets, like YouTube-8M, are free, but most carry significant price tags.

Evaluate the budgets available for data acquisition -rights to datasets like Moments in Time, Something-Something, and Kinetics can cost upwards of tens or hundreds of thousands of dollars.

For smaller budgets, free public datasets, open-source data scraping, and synthetic data generation are more accessible options. Weigh the monetary and legal constraints against the utility of the dataset.

By taking these critical criteria into account, selecting a video training source for an AI vision system becomes much more straightforward:

• Diversity – Seeks to capture the full breadth of visual concepts needed at a sufficient scale and from varied conditions and viewpoints.
• Labeling Accuracy – Precise, verified human-annotated labels covering the complete label distribution with adequate sampling and context.
• Relevance – Matching the project’s specific computer vision problem, not generic unrelated video data.
• Accessibility – Freely available under permissive licensing rather than restrictively licensed.
• Budget – Reasonable budget constraints if licensing and fees are required.

With these criteria in mind, let’s survey some leading options for sourcing training videos:

Kinetics

One of the most popular large-scale video datasets, Kinetics contains over 500,000 video clips representing 600 human action classes, such as walking, handshaking, playing instruments, and many sports.

The 10-second clips are sourced from YouTube and manually annotated. 400 to 1000 video examples per class provide sufficient diversity and volume for pre-training and transfer learning for human action recognition.

However, the licensing is restrictive, and access only requires an application process for approved academic/non-commercial use. This limits options for startups and commercial entities.

Moments in Time

Moments in Time consists of one million 3-second annotated video clips showing dynamic events and actions involving people, animals, objects, and nature.

The clips are sourced from YouTube and represent 339 different action categories. The short clips provide diversity but insufficient duration and context for more complex Recognition tasks. Licensing is free for non-commercial research only.

YouTube-8M

With over 7 million YouTube video IDs spanning 4800 visual entities, YouTube-8M offers free large-scale video data. The clips have machine-generated labels for objects, scenes, actions, etc, which can be noisy.

As a public YouTube dataset, it provides diverse real-world videos. However, the clips are not human-curated or screened for quality, which can pollute training. The source YouTube videos also frequently become unavailable due to removals.

TwentyBN Something-Something

The Something-Something dataset consists of 220,000 video clips depicting 174 different human-object interactions, such as cutting paper, opening doors, and putting things together.

Clips are sourced from crowdworkers performing scripted “something” actions with various objects. The diversity is narrow, but it provides fine-grained human activity analysis. Licensing starts at $16k per year for commercial use.

Looking and Asking for Help

Looking and EPIC-Kitchens – contain first-person videos capturing hands and objects from head-mounted GoPro cameras. These are labeled for human activities, affordances, and verb-noun interactions.

It is great for training models on hand-object coordination and egocentric understanding. Looking is released under permissive licensing, while EPIC is free for non-commercial use.

Custom Data Collection

Creating a custom dataset specific to the environment and use case can be highly beneficial for specialized applications. This allows capturing the diversity of visual concepts in the target domain with customizable labeling.

Data can be collected via static or mobile cameras, crowd-sourcing worker videos, and simulation environments like CARLA. However, custom collection entails significant data engineering effort.

Synthetic Data

An alternative to collecting real-world videos is procedurally generating synthetic training data. This allows the creation of automatic annotations and visual diversity, which are only limited by computation and developer time.

While offering boundless data, synthetic scenes may lack realism. Combining synthetic and real-world data provides an optimal balance. Synthetic data can provide diverse training examples rapidly, while real-world videos add authenticity.

For instance, an interior design AI could train thousands of procedurally generated room interiors to learn general furniture and layout patterns. Then, fine-tuning on real home videos captures realistic textures, lighting, decor styles, and imperfections.

Researchers have shown that combining synthetic and accurate data significantly boosts performance compared to either alone. The synthetic data acts as a regularizer to prevent overfitting to the real-world examples.

Another approach is to use unsupervised and self-supervised pre-training on synthetic data before fine-tuning on limited real-world examples. This leverages vast synthetic datasets more efficiently. Techniques like image-to-image translation can also help bridge the synthetic-to-real gap.

Key Takeaways

• Prioritize diversity of visual concepts from varied conditions relevant to the computer vision problem.
• Larger datasets provide more examples, but labeling accuracy is paramount – precision over quantity.
• Favor public permissive licensing for most excellent access and shareability.
• Match the video source realism, labels, and scope to the target application.
• Combining synthetic data with real-world clips offers an efficient, balanced approach.

There is no one-size-fits-all best video source for training AI. The ideal dataset depends on the problem specifics, visual diversity needed, budgets, licensing, etc. Rigorously evaluating datasets on relevant criteria makes selecting compelling training videos more systematic.

With robust and diverse video data, the possibilities for advancing AI visual intelligence are unlimited. What remains is curating ever-richer video sources to feed the insatiable appetite for deep-learning algorithms. The future promises ever-more-capable computer vision fueled by bursts of pixels beaming knowledge into silicon minds.

Conclusion

Sourcing training videos for AI requires evaluating diversity, labeling, licensing, relevance, and budgets. Favor public diverse datasets relevant to the problem where possible. Mix synthetic data for volume and real video for authenticity. With quality data, computer vision AI is only limited by the pixels provided and creativity in leveraging them.

Frequently Asked Questions

Q: What are some excellent free training videos?

A: Some good accessible sources are YouTube-8M, Moments in Time, Something-Something (non-commercial), public datasets like Kinetics (research only), synthetic data, and open-source scraped data.

Q: Should I prefer diversity or volume of training videos?

A: You need both diversity and sufficient volume. However, a diverse dataset with a modest size is better than a large-scale dataset lacking diversity. Prioritize a variety of visual concepts over just quantity.

Q: How much training video data do I need?

A: As much as possible! But even tens of thousands of clips can be sufficient for narrow domains. For complex vision tasks, datasets with millions of clips are standard. Beyond a point, there are diminishing returns, so balance sufficiency and pragmatism.

Q: Is synthetic video data good enough on its own?

A: Seldom. While synthetic data solves volume and diversity needs, it often lacks realism. Blending synthetic and real-world video combines the best of both. Use synthetic data for pre-training and accurate data for fine-tuning for optimal results.

Q: What if I can’t find a public dataset suitable to my needs?

A: Consider collecting a custom dataset specific to your application, environment, and visual concepts. Or generate synthetic data programmatically tuned to your needs. Mix synthetic and accurate data for efficient training with realism.