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Video Data Collection in 2024: Challenges & Best Practices

A common data type that is used to train computer vision (CV) is video data. As the demand for autonomous vehicles and other computer vision-enabled technologies rises, so does the need for video data since it is considered the fuel that makes these technologies work. 

However, studies show that in the entire development stage of a CV system, the data collection stage often gets neglected.

This article aims to remedy this issue by exploring what video data collection is, what the challenges are in gathering video data and what are some best practices to consider.

If you are interested in working with a data collection partner, here is a guide to the top video data services on the market.

What is video data collection for AI/ML?

Video data collection for AI/ML training is the process of gathering video-object-detection systems, a specific type of video data, to train and deploy a CV system. 

A video dataset can include clips of people, animals, objects, environments, etc. For instance, a video dataset to train a self-driving car might include clips of: 

  • Different vehicles driving on the road,
  • People crossing the road or walking on the sidewalk,
  • Animals or pets crossing the road or on the sidewalk
  • Other objects  on the road or sidewalk (such as street signs, barriers, etc.)
Data with labels of different people, objects, and the road to train different detectors in a self-driving vehicle.
Source: researchgate

What are the challenges in collecting video data?

Data collectors who collect video data might face the following challenges:

1. Cost of collection

Collecting video data can be expensive, especially when the dataset is supposed to be large. Even though smartphones are easily available now to record videos, the recordings can be low-resolution. So data collectors have to use expensive cameras to capture high-quality recordings.

In addition, recording videos on large scales requires extra labor, which can be an expensive process for diverse datasets. 

2. Time-consuming

Gathering video data can be time-consuming since they take longer to record as compared to image data. 

For instance, if a CV-enabled security surveillance system requires data to be collected at a specific time of the day (at dawn, for example), then such data will take significantly longer to collect as compared to data collected during the daytime. This is because the data collector will have a limited time window to record such videos. This issue might arise for image data collection as well; however, taking photos takes significantly less time than recording videos.

3. Unbiased/diverse data collection

A study by Georgia tech identified that computer vision systems are surprisingly good at detecting pedestrians with light skin color. With autonomous vehicles, this kind of discrimination can be fatal if the technology doesn’t detect people of different skin colors. For instance, Tesla’s system did not recognize horse carriages on the road since the system was never trained with horse carriage video data. 

Therefore, collecting diverse video data to avoid such biases and errors can become a challenge if done in-house, even for big companies such as Tesla. 

What are some best practices for video data collection?

While collecting video data, you can consider the following best practices:

1. Automate video data collection

Video data collection can be automated by using web scraping tools. The user can set parameters for the required data that each video should have, which allows the scraper bot to be specific about gathering the relevant data from the internet. 

2. Leverage crowdsourcing

Another effective method of gathering diverse and large datasets is through crowdsourcing. 

Through a crowdsourcing model, contributors around the world can be hired through a platform to complete mini video data collection tasks. There are third-party crowdsourcing data collection specialists for companies to reach out to avoid the hassle of developing a crowdsourcing platform in-house. 

To learn more about crowdsourcing data collection, check out this quick read.

Like every other type of data, gathering video data can also have some legal and ethical baggage. For instance, collecting videos of people for a face detection system can be subjected to some rules and policies that are important to consider in some countries such as the US.

To learn more about why data collection ethics is important and to achieve it check out this quick read. 

4. Ensure data quality

While collecting video data, maintaining the level of quality is very important for the overall performance of the CV system. 

The video data should be:

  • Recorded with consistency – i.e., with similar resolution, light variations, angles, etc.
  • Recorded with diversity in mind. The data should be all-inclusive and comprehensive vis-a-vis the subject for which the data is being collected for.
  • The video data should be authentic and should not have been physically or digitally modified.

To learn more about data collection quality and how to main it, check out this quick read.

For more in-depth knowledge on data collection, feel free to download our whitepaper:

Get Data Collection Whitepaper

You can also check our data-driven list of data collection/harvesting services to find the option that best suits your project needs.

Further reading

If you need help finding a vendor or have any questions, feel free to contact us:

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Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Shehmir Javaid
Shehmir Javaid is an industry analyst in AIMultiple. He has a background in logistics and supply chain technology research. He completed his MSc in logistics and operations management and Bachelor's in international business administration From Cardiff University UK.

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