Video data is essential for training computer vision (CV) systems, especially with the growing demand for autonomous vehicles and CV-enabled technologies. Here, we explore what video data collection entails, the challenges involved, and best practices to consider.
What is video data collection for AI?
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.)
You can also work with a video data collection service provider.
Video data collection challenges
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 it takes longer to record than 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.1 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.2
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.
4. Privacy concerns
A major issue surrounding video data collection is privacy. With so much video being recorded in public spaces, workplaces, and even homes, there are genuine concerns about how this data is used and who has access to it.
Regulations like the GDPR (General Data Protection Regulation) in Europe have been implemented to protect individuals’ privacy, but there is still a delicate balance between harnessing the power of video data and respecting personal privacy.
5 Data storage and management
Video data requires vast amounts of storage space, especially when collected continuously over long periods of time. Storing, managing, and securing this data can be expensive and technically challenging for businesses and organizations. Cloud storage solutions have alleviated some of these challenges, but they come with their own costs and potential security risks.
6. Data overload
The sheer volume of video data being collected can lead to data overload. Without the right tools and technologies, it can be overwhelming for businesses to sift through hours of footage to find the insights they need. This is where AI and machine learning become essential, as they allow for the automated analysis of video data at scale.
Video data collection best practices
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.
More on crowdsourcing data
3. Consider ethical and legal factors
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.
More on data collection ethics.
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.
More on quality assurance while gathering data.
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
External resources
- 1. Predictive Inequity in Object Detection. Accessed: 27/Sep/2024.
- 2. Tesla struggles to identify horse-drawn carriage.
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