Video data powers today‘s most exciting computer vision (CV) applications, from self-driving cars to augmented reality. But collecting quality video data comes with significant hurdles. This comprehensive guide examines key challenges in gathering video data and provides best practices to streamline the process. You‘ll learn insider tips to build the robust video datasets your CV systems need to perform their best in the real world.
The Growing Importance of Video Data
Video data collection is exploding. According to ResearchAndMarkets.com, the video data market will reach $30 billion by 2027, up from just $11 billion in 2020.
Two major factors are fueling this demand:
- More complex CV applications. Technologies like self-driving cars and augmented reality require incredibly diverse video data showing objects and environments in all settings.
- AI and deep learning. Advanced CV systems can only get accurate if they have huge volumes of video data to learn from. According to one estimate, a self-driving car system requires over 100,000 hours of video across various conditions to train properly!
The bottom line? Video data collection will only grow more crucial as CV technology expands into new industries and use cases. Companies able to build robust video datasets will have a real competitive advantage.
The Challenges of Video Data Collection
While great video data leads to better CV systems, collecting that video data comes with significant hurdles:
Building enterprise-grade video datasets gets expensive very quickly:
- Professional cameras for capturing high-quality, high-resolution video can cost $15,000 or more. Consumer cameras don‘t cut it.
- Storing huge video files requires massive investments in cloud storage and servers. You‘ll need redundant backups too.
- If collecting video across countries, the travel costs add up rapidly.
- Labor is expensive. You need teams in the field recording video, and staff to label and validate the data.
All in, companies spend millions building the video datasets needed for accurate CV systems.
Recording quality video just takes more time versus images:
- Long recording durations. Getting a diverse video dataset means capturing behavior across time – not just snapshots. This means long recordings.
- Narrow time windows. You may need to film at specific times, like dusk or night. If you miss that window, you have to wait until the next day.
- According to research by Scale, annotating video data takes ~3x longer than images due to the added time dimension.
Bottom line: you need patience and planning when collecting quality video.
Biases and Gaps
Diverse, unbiased video data is key for an accurate CV system. However, most collected datasets have issues:
- A study by Georgia Tech found CV systems well-trained to detect light-skinned pedestrians, but failed to detect dark-skinned pedestrians 39% of the time. This can lead to serious accidents.
-According to Veoneer research, 70% of AV companies find gaps or flaws in their video datasets down the line. Finding diverse data upfront prevents errors.
Biases can creep in easily if you record video in limited locations, times, and weather. Diverse video data matters.
Best Practices for Collecting Video Data
Given these challenges, what are some best practices as you build video datasets?
Use Web Scraping to Automate
Web scraping tools can rapidly gather online video clips matching your parameters. This automates a very manual process. Just be sure to follow proper licensing rules if using scraped vids in your dataset. Some tools we recommend:
- Octoparse – Scrape video from nearly any site. Great for discovering diverse samples across the web.
- Beautiful Soup – A developer-focused tool that lets you customize scraping of videos sources like YouTube.
Crowdsource from a Global Network
Recording video in every geography and setting needed is nearly impossible solo. That‘s where crowdsourcing shines. Through crowdsourcing platforms, you can coordinate video collection across thousands of contributors worldwide. Some top platforms:
- Labelbox – Specializes in video data collection for CV systems. Handles contributor management.
- Appen – Has a community of 1 million+ global contributors. Compliance focused.
Pro tip: Provide very clear protocols so contributors capture consistent, compliant video.
Watch for Legal and Ethical Concerns
Recording video, especially with people‘s faces, raises important legal and ethical considerations:
- In the EU, GDPR requires consent before capturing identifiable video of individuals. Fines for violations can be upwards of 20 million euros!
- In the US, states often require dual-party consent for recording.
- Beyond laws, think ethically about how video data collection impacts privacy and vulnerable groups.
Consult local laws, and get release forms when needed.
Verify Quality Throughout the Process
With video data collection, it‘s not enough to just record – you must verify quality:
- Ensure footage has adequate resolution and framerates for the CV system – don‘t just use mobile phones.
- Review multiple samples from contributors to check for consistency in camera angle, lighting, etc.
- Store raw footage securely. Any edits or modifications can reduce quality.
- Actively check for gaps in subjects, weather, times of day, geography, skin tones, etc. Diverse sampling prevents bias.
- Spot check label quality. Even 15 minutes of inaccurate labels can undermine algorithm performance.
Set clear protocols, and regularly audit to ensure adherence.
Key Takeaways on Video Data Collection
Here are some core lessons on collecting quality video data:
- Video powers the most exciting CV applications today, from AVs to AR. Plan ample time and budget.
- Challenges like cost, time, and bias exist. Focus on automation, crowdsourcing, ethics, and quality to overcome them.
- Follow best practices around web scraping, global crowdsourcing, legal compliance, and continuous verification.
- Robust video data leads directly to better CV system performance. Invest early in building diverse datasets.